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Merkurjev E, Nguyen DD, Wei GW. Multiscale Laplacian Learning. APPL INTELL 2023; 53:15727-15746. [PMID: 38031564 PMCID: PMC10686291 DOI: 10.1007/s10489-022-04333-2] [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] [Accepted: 11/08/2022] [Indexed: 11/29/2022]
Abstract
Machine learning has greatly influenced many fields, including science. However, despite of the tremendous accomplishments of machine learning, one of the key limitations of most existing machine learning approaches is their reliance on large labeled sets, and thus, data with limited labeled samples remains a challenge. Moreover, the performance of machine learning methods often severely hindered in case of diverse data, usually associated with smaller data sets or data associated with areas of study where the size of the data sets is constrained by high experimental cost and/or ethics. These challenges call for innovative strategies for dealing with these types of data. In this work, the aforementioned challenges are addressed by integrating graph-based frameworks, semi-supervised techniques, multiscale structures, and modified and adapted optimization procedures. This results in two innovative multiscale Laplacian learning (MLL) approaches for machine learning tasks, such as data classification, and for tackling data with limited samples, diverse data, and small data sets. The first approach, multikernel manifold learning (MML), integrates manifold learning with multikernel information and incorporates a warped kernel regularizer using multiscale graph Laplacians. The second approach, the multiscale MBO (MMBO) method, introduces multiscale Laplacians to the modification of the famous classical Merriman-Bence-Osher (MBO) scheme, and makes use of fast solvers. We demonstrate the performance of our algorithms experimentally on a variety of benchmark data sets, and compare them favorably to the state-of-art approaches.
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Affiliation(s)
| | - Duc Duy Nguyen
- Department of Mathematics, University of Kentucky, KY 40506, USA
| | - Guo-Wei Wei
- Department of Mathematics, Department of Biochemistry and Molecular Biology, Department of Electrical and Computer Engineering Michigan State University, MI 48824, USA
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2
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Li J, Pan W, Huang H, Pan J, Wang F. STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition. Front Hum Neurosci 2023; 17:1169949. [PMID: 37125349 PMCID: PMC10133470 DOI: 10.3389/fnhum.2023.1169949] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 03/27/2023] [Indexed: 05/02/2023] Open
Abstract
Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience research. In this paper, we introduce a novel graph neural network called the spatial-temporal graph attention network with a transformer encoder (STGATE) to learn graph representations of emotion EEG signals and improve emotion recognition performance. In STGATE, a transformer-encoder is applied for capturing time-frequency features which are fed into a spatial-temporal graph attention for emotion classification. Using a dynamic adjacency matrix, the proposed STGATE adaptively learns intrinsic connections between different EEG channels. To evaluate the cross-subject emotion recognition performance, leave-one-subject-out experiments are carried out on three public emotion recognition datasets, i.e., SEED, SEED-IV, and DREAMER. The proposed STGATE model achieved a state-of-the-art EEG-based emotion recognition performance accuracy of 90.37% in SEED, 76.43% in SEED-IV, and 76.35% in DREAMER dataset, respectively. The experiments demonstrated the effectiveness of the proposed STGATE model for cross-subject EEG emotion recognition and its potential for graph-based neuroscience research.
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Affiliation(s)
| | | | | | | | - Fei Wang
- School of Software, South China Normal University, Guangzhou, China
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3
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Cevikalp H, Saglamlar H. Transductive Polyhedral Conic Classifiers for Machine Learning Applications. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.07.001] [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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4
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Bai L, Chen X, Wang Z, Shao YH. Safe intuitionistic fuzzy twin support vector machine for semi-supervised learning. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108906] [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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5
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Li D, Dick S. Semi-supervised multi-label classification using an extended graph-based manifold regularization. COMPLEX INTELL SYST 2022; 8:1561-1577. [PMID: 35535331 PMCID: PMC9054917 DOI: 10.1007/s40747-021-00611-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 12/03/2021] [Indexed: 11/28/2022]
Abstract
Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.
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Affiliation(s)
- Ding Li
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB Canada T6G 1H9
| | - Scott Dick
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB Canada T6G 1H9
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Yuan J, Ran X, Liu K, Yao C, Yao Y, Wu H, Liu Q. Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review. J Neurosci Methods 2021; 368:109441. [PMID: 34942271 DOI: 10.1016/j.jneumeth.2021.109441] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 10/23/2021] [Accepted: 12/11/2021] [Indexed: 02/07/2023]
Abstract
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: (i) the conventional machine learning approach combining manual feature engineering and classifiers, (ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.
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Affiliation(s)
- Jie Yuan
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Xuming Ran
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Keyin Liu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Chen Yao
- Shenzhen Second People's Hospital, Shenzhen 518035, PR China
| | - Yi Yao
- Shenzhen Children's Hospital, Shenzhen 518017, PR China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, Macau
| | - Quanying Liu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China.
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7
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Ramp sparse support matrix machine and its application in roller bearing fault diagnosis. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107928] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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8
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Sun Y, Ding S, Zhang Z, Zhang C. Hypergraph based semi-supervised support vector machine for binary and multi-category classifications. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01452-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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9
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Singla M, Ghosh D, Shukla KK. pin ¯ -TSVM: A Robust Transductive Support Vector Machine and its Application to the Detection of COVID-19 Infected Patients. Neural Process Lett 2021; 53:3981-4010. [PMID: 34305439 PMCID: PMC8286050 DOI: 10.1007/s11063-021-10578-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 11/29/2022]
Abstract
Training a machine learning model on the data sets with missing labels is a challenging task. Not all models can handle the problem of missing labels. However, if these data sets are further corrupted with label noise, it becomes even more challenging to train a machine learning model on such data sets. We propose to use a transductive support vector machine (TSVM) for semi-supervised learning in this situation. We make this model robust to label noise by using a truncated pinball loss function with it. We name our approach, pin ¯ -TSVM. We provide both the primal and the dual formulations of the obtained robust TSVM for linear and non-linear kernels. We also perform experiments on synthetic and real-world data sets to prove the superior robustness of our model as compared to the existing approaches. To this end, we use small as well as large-scale data sets to perform the experiments. We show that the model is capable of training in the presence of label noise and finding the missing labels of the data samples. We use this property of pin ¯ -TSVM to detect the coronavirus patients based on their chest X-ray images.
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Affiliation(s)
- Manisha Singla
- Computer Science and Engineering Department, Indian Institute of Technology (Banaras hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Debdas Ghosh
- Department of Mathematical Sciences, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - K. K. Shukla
- Computer Science and Engineering Department, Indian Institute of Technology (Banaras hindu University), Varanasi, Uttar Pradesh 221005 India
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Tan X, Guo C, Jiang T, Fu K, Zhou N, Yuan J, Zhang G. A new semi-supervised algorithm combined with MCICA optimizing SVM for motion imagination EEG classification. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This paper proposed a new semi-supervised algorithm combined with Mutual-cross Imperial Competition Algorithm (MCICA) optimizing Support Vector Machine (SVM) for motion imagination EEG classification, which not only reduces the tedious and time-consuming training process and enhances the adaptability of Brain Computer Interface (BCI), but also utilizes the MCICA to optimize the parameters of SVM in the semi-supervised process. This algorithm combines mutual information and cross validation to construct objective function in the semi-supervised training process, and uses the constructed objective function to establish the semi-supervised model of MCICA for optimizing the parameters of SVM, and finally applies the selected optimal parameters to the data set Iva of 2005 BCI competition to verify its effectiveness. The results showed that the proposed algorithm is effective in optimizing parameters and has good robustness and generalization in solving small sample classification problems.
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Affiliation(s)
- Xuemin Tan
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Chao Guo
- State Grid Chengdu Power Supply Company, Chengdu, Sichuan, China
| | - Tao Jiang
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Kechang Fu
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Nan Zhou
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Jianying Yuan
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Guoliang Zhang
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
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Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.118] [Citation(s) in RCA: 312] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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12
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13
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Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:2087132. [PMID: 31885530 PMCID: PMC6925734 DOI: 10.1155/2019/2087132] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 09/20/2019] [Accepted: 10/10/2019] [Indexed: 11/18/2022]
Abstract
Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework. We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature. Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions. In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set. Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable. In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used. Therefore, our algorithms can provide an alternative way to reduce the calibration time.
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Wu G, Zhang D, Chen W, Zuo W, Xia Z. Robust Deep Softmax Regression Against Label Noise for Unsupervised Domain Adaptation. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001419400020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Domain adaptation aims to generalize the classification model from a source domain to a different but related target domain. Recent studies have revealed the benefit of deep convolutional features trained on a large dataset (e.g. ImageNet) in alleviating domain discrepancy. However, literatures show that the transferability of features decreases as (i) the difference between the source and target domains increases, or (ii) the layers are toward the top layers. Therefore, even with deep features, domain adaptation remains necessary. In this paper, we propose a novel unsupervised domain adaptation (UDA) model for deep neural networks, which is learned with the labeled source samples and the unlabeled target ones simultaneously. For target samples without labels, pseudo labels are assigned to them according to their maximum classification scores during training of the UDA model. However, due to the domain discrepancy, label noise generally is inevitable, which degrades the performance of the domain adaptation model. Thus, to effectively utilize the target samples, three specific robust deep softmax regression (RDSR) functions are performed for them with high, medium and low classification confidence respectively. Extensive experiments show that our method yields the state-of-the-art results, demonstrating the effectiveness of the robust deep softmax regression classifier in UDA.
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Affiliation(s)
- Guangbin Wu
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, P. R. China
| | - David Zhang
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P. R. China
| | - Weishan Chen
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, P. R. China
| | - Wangmeng Zuo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, P. R. China
| | - Zhuang Xia
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, P. R. China
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Li M, Xie L, Wang Z. A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals. SENSORS 2019; 19:s19020429. [PMID: 30669646 PMCID: PMC6359102 DOI: 10.3390/s19020429] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 01/06/2019] [Accepted: 01/18/2019] [Indexed: 01/04/2023]
Abstract
Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution and sample imbalance. In this work, we proposed a framework for real-time stress recognition using peripheral physiological signals, which aimed to reduce the errors caused by individual differences and to improve the regressive performance of stress recognition. The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. For prediction, we selected the epsilon-support vector regression (e-SVR) to construct the transductive model. The non-linear real-time features were extracted using a combination of wavelet packet decomposition and bi-spectrum analysis. The performance of the proposed approach was evaluated using the DEAP dataset and Stroop training. The results indicated the effectiveness of the transductive model, which had a better prediction performance compared to traditional methods. Furthermore, the real-time interactive experiment was conducted in field studies to explore the usability of the proposed framework.
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Affiliation(s)
- Minjia Li
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Lun Xie
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Zhiliang Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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