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Liu M, Yang Z, Han W, Xie S. Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-view Clustering. Neural Netw 2024; 179:106503. [PMID: 38986189 DOI: 10.1016/j.neunet.2024.106503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/09/2024] [Accepted: 06/29/2024] [Indexed: 07/12/2024]
Abstract
Fusion-style Deep Multi-view Clustering (FDMC) can efficiently integrate comprehensive feature information from latent embeddings of multiple views and has drawn much attention recently. However, existing FDMC methods suffer from the interference of view-specific information for fusion representation, affecting the learning of discriminative cluster structure. In this paper, we propose a new framework of Progressive Neighbor-masked Contrastive Learning for FDMC (PNCL-FDMC) to tackle the aforementioned issues. Specifically, by using neighbor-masked contrastive learning, PNCL-FDMC can explicitly maintain the local structure during the embedding aggregation, which is beneficial to the common semantics enhancement on the fusion view. Based on the consistent aggregation, the fusion view is further enhanced by diversity-aware cluster structure enhancement. In this process, the enhanced cluster assignments and cluster discrepancies are employed to guide the weighted neighbor-masked contrastive alignment of semantic structure between individual views and the fusion view. Extensive experiments validate the effectiveness of the proposed framework, revealing its ability in discriminative representation learning and improving clustering performance.
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Affiliation(s)
- Mingyang Liu
- School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Zuyuan Yang
- School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China; Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou 510006, China.
| | - Wei Han
- Guangzhou Railway Polytechnic, Guangzhou, 511300, China.
| | - Shengli Xie
- School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China; Key Laboratory of iDetection and Manufacturing-IoT, Ministry of Education, Guangzhou 510006, China.
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2
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Zhao M, Yang W, Nie F. Deep graph reconstruction for multi-view clustering. Neural Netw 2023; 168:560-568. [PMID: 37837745 DOI: 10.1016/j.neunet.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 07/01/2023] [Accepted: 10/01/2023] [Indexed: 10/16/2023]
Abstract
Graph-based multi-view clustering methods have achieved impressive success by exploring a complemental or independent graph embedding with low-dimension among multiple views. The majority of them, however, are shallow models with limited ability to learn the nonlinear information in multi-view data. To this end, we propose a novel deep graph reconstruction (DGR) framework for multi-view clustering, which contains three modules. Specifically, a Multi-graph Fusion Module (MFM) is employed to obtain the consensus graph. Then node representation is learned by the Graph Embedding Network (GEN). To assign clusters directly, the Clustering Assignment Module (CAM) is devised to obtain the final low-dimensional graph embedding, which can serve as the indicator matrix. In addition, a simple and powerful loss function is designed in the proposed DGR. Extensive experiments on seven real-world datasets have been conducted to verify the superior clustering performance and efficiency of DGR compared with the state-of-the-art methods.
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Affiliation(s)
- Mingyu Zhao
- School of Computer Science, Fudan University, Shanghai 200433, PR China.
| | - Weidong Yang
- School of Computer Science, Fudan University, Shanghai 200433, PR China.
| | - Feiping Nie
- School of Computer Science, School of Artificial Intelligence, Optics and Electronics (iOPEN), and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, PR China.
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3
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Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, Bayer J, Menssink JM, Wang T, Bergmeir C, Wood S, Cotton SM. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res 2023; 327:115265. [PMID: 37348404 DOI: 10.1016/j.psychres.2023.115265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.
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Affiliation(s)
- Caroline X Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia; Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Ye Zhu
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Catherine L Smith
- Department of Epidemiology and Preventative Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Lan Du
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Kate M Filia
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Johanna Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Jana M Menssink
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Teresa Wang
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Christoph Bergmeir
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Stephen Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
| | - Sue M Cotton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia; Orygen, Parkville, VIC, Australia
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4
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Li C, Che H, Leung MF, Liu C, Yan Z. Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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5
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Paul D, Chakraborty S, Das S, Xu J. Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:5862-5871. [PMID: 36282831 DOI: 10.1109/tpami.2022.3217137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature nonlinear and multi-view separation. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from the non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power k-Means, our algorithm uses majorization-minimization (MM) to better solve this non-convex problem. We show that the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates. We rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees as well as finite-sample bounds on the excess risk of the estimates through modern tools in learning theory. The proposal's efficacy is demonstrated through an array of simulated and real data experiments.
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6
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Şenol A. MCMSTClustering: defining non-spherical clusters by using minimum spanning tree over KD-tree-based micro-clusters. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08386-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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7
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Deng P, Li T, Wang D, Wang H, Peng H, Horng SJ. Multi-view clustering guided by unconstrained non-negative matrix factorization. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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8
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Robust and Optimal Neighborhood Graph Learning for Multi-View Clustering. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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9
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Chen ZS, Wilson MA. How our understanding of memory replay evolves. J Neurophysiol 2023; 129:552-580. [PMID: 36752404 PMCID: PMC9988534 DOI: 10.1152/jn.00454.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, United States
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, New York, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, New York, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York, United States
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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10
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Zhao M, Yang W, Nie F. Auto-weighted Orthogonal and Nonnegative Graph Reconstruction for Multi-view Clustering. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
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11
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Inclusivity induced adaptive graph learning for multi-view clustering. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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12
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Mixed structure low-rank representation for multi-view subspace clustering. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04474-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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13
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Zhang Y, Kiryu H. MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes. Brief Bioinform 2022; 23:6696139. [PMID: 36094092 DOI: 10.1093/bib/bbac372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/16/2022] [Accepted: 08/08/2022] [Indexed: 12/14/2022] Open
Abstract
The identification of cancer subtypes can help researchers understand hidden genomic mechanisms, enhance diagnostic accuracy and improve clinical treatments. With the development of high-throughput techniques, researchers can access large amounts of data from multiple sources. Because of the high dimensionality and complexity of multiomics and clinical data, research into the integration of multiomics data is needed, and developing effective tools for such purposes remains a challenge for researchers. In this work, we proposed an entirely unsupervised clustering method without harnessing any prior knowledge (MODEC). We used manifold optimization and deep-learning techniques to integrate multiomics data for the identification of cancer subtypes and the analysis of significant clinical variables. Since there is nonlinearity in the gene-level datasets, we used manifold optimization methodology to extract essential information from the original omics data to obtain a low-dimensional latent subspace. Then, MODEC uses a deep learning-based clustering module to iteratively define cluster centroids and assign cluster labels to each sample by minimizing the Kullback-Leibler divergence loss. MODEC was applied to six public cancer datasets from The Cancer Genome Atlas database and outperformed eight competing methods in terms of the accuracy and reliability of the subtyping results. MODEC was extremely competitive in the identification of survival patterns and significant clinical features, which could help doctors monitor disease progression and provide more suitable treatment strategies.
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Affiliation(s)
- Yanting Zhang
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan
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14
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Scalable one-stage multi-view subspace clustering with dictionary learning. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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15
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Liu M, Yang Z, Li L, Li Z, Xie S. Auto-weighted collective matrix factorization with graph dual regularization for multi-view clustering. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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One-step incomplete multiview clustering with low-rank tensor graph learning. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.103054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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18
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Hua Y, Wan F, Liao B, Zong Y, Zhu S, Qing X. Adaptive multitask clustering algorithm based on distributed diffusion least-mean-square estimation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.05.074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Wang S, Chen Y, Yi S, Chao G. Frobenius norm-regularized robust graph learning for multi-view subspace clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03816-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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20
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Liang N, Yang Z, Li Z, Han W. Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03551-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Fusing Local and Global Information for One-Step Multi-View Subspace Clustering. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.
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22
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Liu L, Chen P, Luo G, Kang Z, Luo Y, Han S. Scalable multi-view clustering with graph filtering. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07326-x] [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]
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23
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Face aging with pixel-level alignment GAN. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03541-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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25
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Liu M, Yang Z, Han W, Chen J, Sun W. Semi-supervised multi-view binary learning for large-scale image clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03205-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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26
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Wang S, Chen Y, Cen Y, Zhang L, Wang H, Voronin V. Nonconvex low-rank and sparse tensor representation for multi-view subspace clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03406-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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27
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Si X, Yin Q, Zhao X, Yao L. Robust deep multi-view subspace clustering networks with a correntropy-induced metric. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03209-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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28
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Auto-Weighted Graph Regularization and Residual Compensation for Multi-view Subspace Clustering. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10789-7] [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]
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29
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Huang J, Ding W, Lv J, Yang J, Dong H, Del Ser J, Xia J, Ren T, Wong ST, Yang G. Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information. APPL INTELL 2022; 52:14693-14710. [PMID: 36199853 PMCID: PMC9526695 DOI: 10.1007/s10489-021-03092-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2021] [Indexed: 12/24/2022]
Abstract
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.
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Affiliation(s)
- Jiahao Huang
- College of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, 226019 Nantong, China
| | - Jun Lv
- School of Computer and Control Engineering, Yantai University, 264005 Yantai, China
| | - Jingwen Yang
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing, China
| | - Hao Dong
- Center on Frontiers of Computing Studies, Peking University, Beijing, China
| | - Javier Del Ser
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Jun Xia
- Department of Radiology, Shenzhen Second People’s Hospital, The First Afliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Tiaojuan Ren
- College of Information Science and Technology, Zhejiang Shuren University, 310015 Hangzhou, China
| | - Stephen T. Wong
- Systems Medicine and Bioengineering Department, Departments of Radiology and Pathology, Houston Methodist Cancer Center, Houston Methodist Hospital, Weill Cornell Medicine, 77030 Houston, TX USA
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
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31
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Dong W, Wu XJ, Xu T. Multi-view Subspace Clustering via Joint Latent Representations. Neural Process Lett 2022. [DOI: 10.1007/s11063-021-10710-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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32
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El Hajjar S, Dornaika F, Abdallah F, Barrena N. Consensus graph and spectral representation for one-step multi-view kernel based clustering. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108250] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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