1
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Lila E, Zhang W, Rane Levendovszky S. Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease. J R Stat Soc Series B Stat Methodol 2024; 86:1013-1044. [PMID: 39279915 PMCID: PMC11398888 DOI: 10.1093/jrsssb/qkae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/15/2024] [Accepted: 02/19/2024] [Indexed: 09/18/2024]
Abstract
We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with appropriate differential regularization. Our approach does not require prior estimation of the covariance structure of the functional predictors, which is computationally prohibitive in our application setting. We provide a theoretical analysis of the out-of-sample prediction error of the proposed model and explore the finite sample performance in a simulation setting. We apply the proposed method to a pooled dataset from Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative. Through this application, we identify discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease that are consistent with the existing neuroscience literature.
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Affiliation(s)
- Eardi Lila
- Department of Biostatistics, University of Washington, Seattle, USA
| | - Wenbo Zhang
- Department of Biostatistics, University of Washington, Seattle, USA
- Department of Statistics, University of California, Irvine, USA
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2
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Xue K, Yang J, Yao F. Optimal linear discriminant analysis for high-dimensional functional data. J Am Stat Assoc 2023. [DOI: 10.1080/01621459.2022.2164288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Kaijie Xue
- School of Statistics and Data Science, Nankai University, Tianjin, China
| | - Jin Yang
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20852, U.S.A
| | - Fang Yao
- Department of Probability and Statistics, School of Mathematical Sciences, Center for Statistical Science, Peking University, Beijing, China
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3
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Atkins S, Einarsson G, Clemmensen L, Ames B. Proximal methods for sparse optimal scoring and discriminant analysis. ADV DATA ANAL CLASSI 2022. [DOI: 10.1007/s11634-022-00530-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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4
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A unified precision matrix estimation framework via sparse column-wise inverse operator under weak sparsity. ANN I STAT MATH 2022. [DOI: 10.1007/s10463-022-00856-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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5
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Anzarmou Y, Mkhadri A, Oualkacha K. Sparse overlapped linear discriminant analysis. TEST-SPAIN 2022. [DOI: 10.1007/s11749-022-00839-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Wang H, Wang X, Li T, Lai D, Zhang YD. Adverse effect signature extraction and prediction for drugs treating COVID-19. Front Genet 2022; 13:1019940. [DOI: 10.3389/fgene.2022.1019940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Given the considerable cost of drug discovery, drug repurposing is becoming attractive as it can effectively shorten the development timeline and reduce the development cost. However, most existing drug-repurposing methods omitted the heterogeneous health conditions of different COVID-19 patients. In this study, we evaluated the adverse effect (AE) profiles of 106 COVID-19 drugs. We extracted four AE signatures to characterize the AE distribution of 106 COVID-19 drugs by non-negative matrix factorization (NMF). By integrating the information from four distinct databases (AE, bioassay, chemical structure, and gene expression information), we predicted the AE profiles of 91 drugs with inadequate AE feedback. For each of the drug clusters, discriminant genes accounting for mechanisms of different AE signatures were identified by sparse linear discriminant analysis. Our findings can be divided into three parts. First, drugs abundant with AE-signature 1 (for example, remdesivir) should be taken with caution for patients with poor liver, renal, or cardiac functions, where the functional genes accumulate in the RHO GTPases Activate NADPH Oxidases pathway. Second, drugs featuring AE-signature 2 (for example, hydroxychloroquine) are unsuitable for patients with vascular disorders, with relevant genes enriched in signal transduction pathways. Third, drugs characterized by AE signatures 3 and 4 have relatively mild AEs. Our study showed that NMF and network-based frameworks contribute to more precise drug recommendations.
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7
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Guo B, Eberly LE, Henry PG, Lenglet C, Lock EF. Multiway sparse distance weighted discrimination. J Comput Graph Stat 2022; 32:730-743. [PMID: 37377729 PMCID: PMC10292743 DOI: 10.1080/10618600.2022.2099404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/01/2022] [Indexed: 10/17/2022]
Abstract
Modern data often take the form of a multiway array. However, most classification methods are designed for vectors, i.e., 1-way arrays. Distance weighted discrimination (DWD) is a popular high-dimensional classification method that has been extended to the multiway context, with dramatic improvements in performance when data have multiway structure. However, the previous implementation of multiway DWD was restricted to classification of matrices, and did not account for sparsity. In this paper, we develop a general framework for multiway classification which is applicable to any number of dimensions and any degree of sparsity. We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure. For our motivating application, magnetic resonance spectroscopy (MRS) was used to measure the abundance of several metabolites across multiple neurological regions and across multiple time points in a mouse model of Friedreich's ataxia, yielding a four-way data array. Our method reveals a robust and interpretable multi-region metabolomic signal that discriminates the groups of interest. We also successfully apply our method to gene expression time course data for multiple sclerosis treatment. An R implementation is available in the package MultiwayClassification at http://github.com/lockEF/MultiwayClassification.
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Affiliation(s)
- Bin Guo
- Division of Biostatistics, School of Public Health
| | - Lynn E Eberly
- Division of Biostatistics, School of Public Health
- Center for Magnetic Resonance Research, University of Minnesota
| | | | | | - Eric F Lock
- Division of Biostatistics, School of Public Health
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8
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Chen H, Guo Y, He Y, Ji J, Liu L, Shi Y, Wang Y, Yu L, Zhang X. Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity. Biostatistics 2022; 23:967-989. [PMID: 33769450 PMCID: PMC9295187 DOI: 10.1093/biostatistics/kxab007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 02/20/2021] [Accepted: 02/22/2021] [Indexed: 01/03/2023] Open
Abstract
Growing evidence has shown that the brain connectivity network experiences alterations for complex diseases such as Alzheimer's disease (AD). Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multidimensional and in matrix-form. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrices framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of network connections may vary across subjects, we develop an ensemble-learning procedure, which identifies the differential interaction patterns of brain regions between the case group and the control group and conducts medical diagnosis (classification) of the disease simultaneously. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed procedure to the functional connectivity analysis of an functional magnetic resonance imaging study on AD. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies, and satisfactory out-of-sample classification performance is achieved for medical diagnosis of AD.
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Affiliation(s)
- Hao Chen
- School of Statistics, Shandong University of Finance and
Economics, Jinan, 250014, China
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public
Health, Emory University, Atlanta, GA 30322, USA
| | - Yong He
- Institute for Financial Studies, Shandong University, Jinan,
250100, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan,
250100, China
| | - Lei Liu
- Division of Biostatistics, Washington University in St.Louis,
St. Louis, MO 63110, USA
| | - Yufeng Shi
- Institute for Financial Studies, Shandong University, Jinan,
250100, China
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public
Health, Emory University, Atlanta, GA 30322, USA
| | - Long Yu
- Department of Statistics, School of Management, Fudan
University, Shanghai, 200433, China
| | - Xinsheng Zhang
- Department of Statistics, School of Management, Fudan
University, Shanghai, 200433, China
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9
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Chen LP. Nonparametric discriminant analysis with network structures in predictor. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2084618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Li-Pang Chen
- Department of Statistics, National Chengchi University, Taipei, Taiwan
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10
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Fop M, Mattei PA, Bouveyron C, Murphy TB. Unobserved classes and extra variables in high-dimensional discriminant analysis. ADV DATA ANAL CLASSI 2022; 16:55-92. [PMID: 35308632 PMCID: PMC8924148 DOI: 10.1007/s11634-021-00474-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 07/15/2021] [Accepted: 10/03/2021] [Indexed: 11/30/2022]
Abstract
AbstractIn supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions. We introduce a model-based discriminant approach, Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA), which can detect unobserved classes and adapt to the increasing dimensionality. Model estimation is carried out via a full inductive approach based on an EM algorithm. The method is then embedded in a more general framework for adaptive variable selection and classification suitable for data of large dimensions. A simulation study and an artificial experiment related to classification of adulterated honey samples are used to validate the ability of the proposed framework to deal with complex situations.
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Affiliation(s)
- Michael Fop
- School of Mathematics & Statistics, University College Dublin, Dublin, Ireland
| | | | - Charles Bouveyron
- Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France
| | - Thomas Brendan Murphy
- Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France
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11
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Mai Q, He D, Zou H. Coordinatewise Gaussianization: Theories and Applications. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2044825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Qing Mai
- Department of Statistics, Florida State University
| | - Di He
- School of Economics, Nanjing University, Nanjing, 210046, China
| | - Hui Zou
- School of Statistics, University of Minnesota
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12
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Anzarmou Y, Mkhadri A. Classification in high dimension with an Alternative Feature Augmentation via Nonparametrics and Selection (AFANS). COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2021.2024232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Youssef Anzarmou
- Department of Mathematics, Cadi Ayyad University, Marrakech, Morocco
| | - Abdallah Mkhadri
- Department of Mathematics, Cadi Ayyad University, Marrakech, Morocco
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13
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Bao Y, Liu Y. Varying coefficient linear discriminant analysis for dynamic data. Electron J Stat 2022. [DOI: 10.1214/22-ejs2066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Yajie Bao
- School of Mathematical Sciences, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Yuyang Liu
- School of Mathematical Sciences, Shanghai Jiao Tong University, 200240 Shanghai, China
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14
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Min K, Mai Q. A general framework for tensor screening through smoothing. Electron J Stat 2022. [DOI: 10.1214/21-ejs1954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Keqian Min
- Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A
| | - Qing Mai
- Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A
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15
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Ren S, Mai Q. The robust nearest shrunken centroids classifier for high-dimensional heavy-tailed data. Electron J Stat 2022. [DOI: 10.1214/22-ejs2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Shaokang Ren
- Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A
| | - Qing Mai
- Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A
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16
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17
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Chen H, Xia Y. A Normality Test for High-dimensional Data Based on the Nearest Neighbor Approach. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1953507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Hao Chen
- Department of Statistics, University of California at Davis, CA
| | - Yin Xia
- Department of Statistics, School of Management, Fudan University
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18
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Zeng C, Thomas DC, Lewinger JP. Incorporating prior knowledge into regularized regression. Bioinformatics 2021; 37:514-521. [PMID: 32915960 PMCID: PMC8599719 DOI: 10.1093/bioinformatics/btaa776] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 08/13/2020] [Accepted: 09/01/2020] [Indexed: 01/15/2023] Open
Abstract
MOTIVATION Associated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation. RESULTS We propose a new penalized regression approach that allows a priori integration of external meta-features. The method extends LASSO regression by incorporating individualized penalty parameters for each regression coefficient. The penalty parameters are, in turn, modeled as a log-linear function of the meta-features and are estimated from the data using an approximate empirical Bayes approach. Optimization of the marginal likelihood on which the empirical Bayes estimation is performed using a fast and stable majorization-minimization procedure. Through simulations, we show that the proposed regression with individualized penalties can outperform the standard LASSO in terms of both parameters estimation and prediction performance when the external data is informative. We further demonstrate our approach with applications to gene expression studies of bone density and breast cancer. AVAILABILITY AND IMPLEMENTATION The methods have been implemented in the R package xtune freely available for download from https://cran.r-project.org/web/packages/xtune/index.html.
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Affiliation(s)
- Chubing Zeng
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Duncan Campbell Thomas
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
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19
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Cai TT, Zhang L. A convex optimization approach to high-dimensional sparse quadratic discriminant analysis. Ann Stat 2021. [DOI: 10.1214/20-aos2012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- T. Tony Cai
- Department of Statistics, The Wharton School, University of Pennsylvania
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20
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Wang L, Li J, Liu J, Chang M. RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5584684. [PMID: 34122617 PMCID: PMC8172296 DOI: 10.1155/2021/5584684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/12/2021] [Indexed: 11/17/2022]
Abstract
In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.
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Affiliation(s)
- Lei Wang
- Department of Basic Science Teaching, Henan Polytechnic Institute, Nanyang, 473000 Henan, China
| | - Juntao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
| | - Juanfang Liu
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
| | - Mingming Chang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007 Henan, China
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21
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Mai Q, Zhang X, Pan Y, Deng K. A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1904959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Qing Mai
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Xin Zhang
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Yuqing Pan
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Kai Deng
- Department of Statistics, Florida State University, Tallahassee, FL
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22
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High-dimensional linear discriminant analysis with moderately clipped LASSO. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2021. [DOI: 10.29220/csam.2021.28.1.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Nam JH, Kim D, Chung D. Sparse Linear Discriminant Analysis using the Prior-Knowledge-Guided Block Covariance Matrix. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2020; 206:104142. [PMID: 32968333 PMCID: PMC7505231 DOI: 10.1016/j.chemolab.2020.104142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
There are two key challenges when using a linear discriminant analysis in the high-dimensional setting, including singularity of the covariance matrix and difficulty of interpreting the resulting classifier. Although several methods have been proposed to address these problems, they focused only on identifying a parsimonious set of variables maximizing classification accuracy. However, most methods did not consider dependency between variables and efficacy of selected variables appropriately. To address these limitations, here we propose a new approach that directly estimates the sparse discriminant vector without a need of estimating the whole inverse covariance matrix, by formulating a quadratic optimization problem. Furthermore, this approach also allows to integrate external information to guide the structure of covariance matrix. We evaluated the proposed model with simulation studies. We then applied it to the transcriptomic study that aims to identify genomic markers predictive of the response to cancer immunotherapy, where the covariance matrix was constructed based on the prior knowledge available in the pathway database.
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Affiliation(s)
- Jin Hyun Nam
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29412, United States of America
- School of Pharmacy, Sungkyunkwan University, Suwon, Republic of Korea
| | - Donguk Kim
- Department of Statistics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, United States of America
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24
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He Y, Chen H, Sun H, Ji J, Shi Y, Zhang X, Liu L. High-dimensional integrative copula discriminant analysis for multiomics data. Stat Med 2020; 39:4869-4884. [PMID: 33617001 DOI: 10.1002/sim.8758] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 08/30/2020] [Accepted: 09/04/2020] [Indexed: 11/08/2022]
Abstract
Multiomics or integrative omics data have been increasingly common in biomedical studies, holding a promise in better understanding human health and disease. In this article, we propose an integrative copula discrimination analysis classifier in the context of two-class classification, which relaxes the common Gaussian assumption and gains power by borrowing information from multiple omics data types in discriminant analysis. Numerical studies are conducted to assess the finite sample performance of the new classifier. We apply our model to the Religious Orders Study and Memory and Aging Project (ROSMAP) Study, integrating gene expression and DNA methylation data for better prediction.
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Affiliation(s)
- Yong He
- Shandong University, Jinan, China
| | - Hao Chen
- School of Statistics, Shandong University of Finance and Economics, Jinan, China
| | - Hao Sun
- School of Statistics, Shandong University of Finance and Economics, Jinan, China
| | | | - Yufeng Shi
- Shandong University, Jinan, China.,School of Statistics, Shandong University of Finance and Economics, Jinan, China
| | | | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
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25
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Affiliation(s)
- Zhi Ji
- School of Mathematics and StatisticsLanzhou University Lanzhou China
| | - Yang Wei
- School of Mathematics and StatisticsLanzhou University Lanzhou China
| | - Zhouping Li
- School of Mathematics and StatisticsLanzhou University Lanzhou China
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26
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Wu Y, Yu G. Weighted linear programming discriminant analysis for high‐dimensional binary classification. Stat Anal Data Min 2020. [DOI: 10.1002/sam.11473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Yufei Wu
- Department of BiostatisticsUniversity at Buffalo, The State University of New York Buffalo New York USA
| | - Guan Yu
- Department of BiostatisticsUniversity at Buffalo, The State University of New York Buffalo New York USA
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27
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Luo S, Chen Z. A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2020.104641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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28
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Chen H, He Y, Ji J, Shi Y. The sparse group lasso for high-dimensional integrative linear discriminant analysis with application to alzheimer's disease prediction. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1800011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Hao Chen
- School of Statistics, Shandong University of Finance and Economics, Jinan, People's Republic of China
| | - Yong He
- Institute for Financial Studies, Shandong University, Jinan, People's Republic of China
| | - Jiadong Ji
- School of Statistics, Shandong University of Finance and Economics, Jinan, People's Republic of China
| | - Yufeng Shi
- School of Statistics, Shandong University of Finance and Economics, Jinan, People's Republic of China
- Institute for Financial Studies, Shandong University, Jinan, People's Republic of China
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29
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Jiang B, Chen Z, Leng C. Dynamic linear discriminant analysis in high dimensional space. BERNOULLI 2020. [DOI: 10.3150/19-bej1154] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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30
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Pan Y, Mai Q. Efficient computation for differential network analysis with applications to quadratic discriminant analysis. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106884] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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31
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Sheng Y, Wang Q. Conditional probability estimation based classification with class label missing at random. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2019.104566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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32
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Gaynanova I. Prediction and estimation consistency of sparse multi-class penalized optimal scoring. BERNOULLI 2020. [DOI: 10.3150/19-bej1126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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33
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A consistent variable selection method in high-dimensional canonical discriminant analysis. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2019.104561] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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34
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Li Y, Zhang L, Maiti T. High dimensional classification for spatially dependent data with application to neuroimaging. Electron J Stat 2020. [DOI: 10.1214/20-ejs1743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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35
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On the strong oracle property of concave penalized estimators with infinite penalty derivative at the origin. J Korean Stat Soc 2020. [DOI: 10.1007/s42952-019-00024-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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36
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Li Y, Hong HG, Li Y. Multiclass linear discriminant analysis with ultrahigh-dimensional features. Biometrics 2019; 75:1086-1097. [PMID: 31009070 PMCID: PMC6810714 DOI: 10.1111/biom.13065] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 03/25/2019] [Indexed: 11/29/2022]
Abstract
Within the framework of Fisher's discriminant analysis, we propose a multiclass classification method which embeds variable screening for ultrahigh-dimensional predictors. Leveraging interfeature correlations, we show that the proposed linear classifier recovers informative features with probability tending to one and can asymptotically achieve a zero misclassification rate. We evaluate the finite sample performance of the method via extensive simulations and use this method to classify posttransplantation rejection types based on patients' gene expressions.
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Affiliation(s)
- Yanming Li
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Hyokyoung G Hong
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan
| | - Yi Li
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
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37
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Abstract
Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due to complicated model structure and high noise level, existing screening methods often suffer from model misspecification and the presence of outliers. To address these issues, we introduce a new metric named cumulative divergence (CD), and develop a CD-based forward screening procedure. This forward screening method is model-free and resistant to the presence of outliers in the response. It also incorporates the joint effects among covariates into the screening process. With a data-driven threshold, the new method can automatically determine the number of features that should be retained after screening. These merits make the CD-based screening very appealing in practice. Under certain regularity conditions, we show that the proposed method possesses sure screening property. The performance of our proposal is illustrated through simulations and a real data example.
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Affiliation(s)
- Tingyou Zhou
- School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, P. R. China
| | - Liping Zhu
- Institute of Statistics and Big Data and Center for Applied Statistics, Renmin University of China, Beijing, P. R. China
| | - Chen Xu
- Department of Mathematics and Statistics University of Ottawa, Ottawa, Canada
| | - Runze Li
- Department of Statistics and The Methodology Center, The Pennsylvania State University at University Park, U.S.A
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38
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Antonelli J, Claggett BL, Henglin M, Kim A, Ovsak G, Kim N, Deng K, Rao K, Tyagi O, Watrous JD, Lagerborg KA, Hushcha PV, Demler OV, Mora S, Niiranen TJ, Pereira AC, Jain M, Cheng S. Statistical Workflow for Feature Selection in Human Metabolomics Data. Metabolites 2019; 9:metabo9070143. [PMID: 31336989 PMCID: PMC6680705 DOI: 10.3390/metabo9070143] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/03/2019] [Accepted: 07/10/2019] [Indexed: 01/02/2023] Open
Abstract
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.
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Affiliation(s)
- Joseph Antonelli
- Department of Statistics, University of Florida, Gainesville, FL 32611, USA
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Brian L Claggett
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mir Henglin
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Andy Kim
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Gavin Ovsak
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nicole Kim
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Katherine Deng
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kevin Rao
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Octavia Tyagi
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeramie D Watrous
- Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Kim A Lagerborg
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Pavel V Hushcha
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Olga V Demler
- Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Samia Mora
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Teemu J Niiranen
- National Institute for Health and Welfare, FI 00271 Helsinki, Finland
- Department of Medicine, Turku University Hospital and Univesity of Turku, FI 20521 Turrku, Finland
| | | | - Mohit Jain
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - Susan Cheng
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
- Framingham Heart Study, Framingham, MA 01701, USA.
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39
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Affiliation(s)
- Wei Hu
- Department of Statistics, University of California, Irvine, CA
| | - Weining Shen
- Department of Statistics, University of California, Irvine, CA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, CA
| | - Dehan Kong
- Department of Statistical Sciences, University of Toronto, Toronto
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40
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Tony Cai T, Zhang L. High dimensional linear discriminant analysis: optimality, adaptive algorithm and missing data. J R Stat Soc Series B Stat Methodol 2019. [DOI: 10.1111/rssb.12326] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- T. Tony Cai
- University of Pennsylvania; Philadelphia USA
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41
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Cai TT, Ma J, Zhang L. CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality. Ann Stat 2019. [DOI: 10.1214/18-aos1711] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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42
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Sheng Y, Wang Q. Simultaneous variable selection and class fusion with penalized distance criterion based classifiers. Comput Stat Data Anal 2019. [DOI: 10.1016/j.csda.2018.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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43
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Xiong H, Cheng W, Bian J, Hu W, Sun Z, Guo Z. DBSDA : Lowering the Bound of Misclassification Rate for Sparse Linear Discriminant Analysis via Model Debiasing. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:707-717. [PMID: 30047901 DOI: 10.1109/tnnls.2018.2846783] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Linear discriminant analysis (LDA) is a well-known technique for linear classification, feature extraction, and dimension reduction. To improve the accuracy of LDA under the high dimension low sample size (HDLSS) settings, shrunken estimators, such as Graphical Lasso, can be used to strike a balance between biases and variances. Although the estimator with induced sparsity obtains a faster convergence rate, however, the introduced bias may also degrade the performance. In this paper, we theoretically analyze how the sparsity and the convergence rate of the precision matrix (also known as inverse covariance matrix) estimator would affect the classification accuracy by proposing an analytic model on the upper bound of an LDA misclassification rate. Guided by the model, we propose a novel classifier, DBSDA , which improves classification accuracy through debiasing. Theoretical analysis shows that DBSDA possesses a reduced upper bound of misclassification rate and better asymptotic properties than sparse LDA (SDA). We conduct experiments on both synthetic datasets and real application datasets to confirm the correctness of our theoretical analysis and demonstrate the superiority of DBSDA over LDA, SDA, and other downstream competitors under HDLSS settings.
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44
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Gaynanova I, Wang T. Sparse quadratic classification rules via linear dimension reduction. J MULTIVARIATE ANAL 2019; 169:278-299. [PMID: 31105355 PMCID: PMC6516858 DOI: 10.1016/j.jmva.2018.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We consider the problem of high-dimensional classification between two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, we propose to perform simultaneous variable selection and linear dimension reduction on the original data, with the subsequent application of quadratic discriminant analysis on the reduced space. In contrast to quadratic discriminant analysis, the proposed framework doesn't require the estimation of precision matrices; it scales linearly with the number of measurements, making it especially attractive for the use on high-dimensional datasets. We support the methodology with theoretical guarantees on variable selection consistency, and empirical comparisons with competing approaches. We apply the method to gene expression data of breast cancer patients, and confirm the crucial importance of the ESR1 gene in differentiating estrogen receptor status.
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Affiliation(s)
- Irina Gaynanova
- Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA
| | - Tianying Wang
- Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA
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45
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Song F, Lai P, Shen B, Cheng G. Variance ratio screening for ultrahigh dimensional discriminant analysis. COMMUN STAT-THEOR M 2018. [DOI: 10.1080/03610926.2017.1406113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Fengli Song
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Peng Lai
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Baohua Shen
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Guosheng Cheng
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing, China
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46
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Liu J, Yu G, Liu Y. Graph-based sparse linear discriminant analysis for high-dimensional classification. J MULTIVARIATE ANAL 2018; 171:250-269. [PMID: 31983784 DOI: 10.1016/j.jmva.2018.12.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Linear discriminant analysis (LDA) is a well-known classification technique that enjoyed great success in practical applications. Despite its effectiveness for traditional low-dimensional problems, extensions of LDA are necessary in order to classify high-dimensional data. Many variants of LDA have been proposed in the literature. However, most of these methods do not fully incorporate the structure information among predictors when such information is available. In this paper, we introduce a new high-dimensional LDA technique, namely graph-based sparse LDA (GSLDA), that utilizes the graph structure among the features. In particular, we use the regularized regression formulation for penalized LDA techniques, and propose to impose a structure-based sparse penalty on the discriminant vector β . The graph structure can be either given or estimated from the training data. Moreover, we explore the relationship between the within-class feature structure and the overall feature structure. Based on this relationship, we further propose a variant of our proposed GSLDA to utilize effectively unlabeled data, which can be abundant in the semi-supervised learning setting. With the new regularization, we can obtain a sparse estimate of β and more accurate and interpretable classifiers than many existing methods. Both the selection consistency of β estimation and the convergence rate of the classifier are established, and the resulting classifier has an asymptotic Bayes error rate. Finally, we demonstrate the competitive performance of the proposed GSLDA on both simulated and real data studies.
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Affiliation(s)
- Jianyu Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Guan Yu
- Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA.,Department of Genetics, Department of Biostatistics, and Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC 27599, USA
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47
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Affiliation(s)
- Yuqing Pan
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Qing Mai
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Xin Zhang
- Department of Statistics, Florida State University, Tallahassee, FL
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48
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Li Q, Li L. Integrative linear discriminant analysis with guaranteed error rate improvement. Biometrika 2018; 105:917-930. [PMID: 31762476 DOI: 10.1093/biomet/asy047] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.
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Affiliation(s)
- Quefeng Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, 3105D McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, U.S.A
| | - Lexin Li
- Division of Biostatistics, University of California at Berkeley, 50 University Hall 7360, Berkeley, California 94720, U.S.A
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49
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Tan KM, Wang Z, Liu H, Zhang T. Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow. J R Stat Soc Series B Stat Methodol 2018. [DOI: 10.1111/rssb.12291] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | | | - Han Liu
- Northwestern University; Evanston USA
| | - Tong Zhang
- Tencent Technology Shenzhen; People's Republic of China
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50
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Li Y, Lei J. Sparse subspace linear discriminant analysis. STATISTICS-ABINGDON 2018. [DOI: 10.1080/02331888.2018.1469020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Yanfang Li
- School of Mathematical Sciences, Peking University, Beijing, People's Republic of China
| | - Jing Lei
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA
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