Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition
In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test...
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| Published in: | Frontiers in neuroscience Vol. 16; p. 855421 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Frontiers Research Foundation
04.05.2022
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| ISSN: | 1662-453X, 1662-4548, 1662-453X |
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| Abstract | In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test data need to be independently identically distributed (IID); any individual user may show a completely different encephalogram (EEG) data in the same situation. The EEG data may be non-IID. In addition, noise/outlier sensitiveness still exist in GSSL approaches. To these ends, we propose in this paper a novel clustering method based on structure risk minimization model, called multi-model adaptation learning with possibilistic clustering assumption for EEG-based emotion recognition (MA-PCA). It can effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some reproduced kernel Hilbert space. Our main ideas are as follows: (1) reducing the negative impact of noise/outlier patterns through fuzzy entropy regularization, (2) considering the training data and test data are IID and non-IID to obtain a better performance by multi-model adaptation learning, and (3) the algorithm implementation and convergence theorem are also given. A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition. |
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| AbstractList | In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test data need to be independently identically distributed (IID); any individual user may show a completely different encephalogram (EEG) data in the same situation. The EEG data may be non-IID. In addition, noise/outlier sensitiveness still exist in GSSL approaches. To these ends, we propose in this paper a novel clustering method based on structure risk minimization model, called multi-model adaptation learning with possibilistic clustering assumption for EEG-based emotion recognition (MA-PCA). It can effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some reproduced kernel Hilbert space. Our main ideas are as follows: (1) reducing the negative impact of noise/outlier patterns through fuzzy entropy regularization, (2) considering the training data and test data are IID and non-IID to obtain a better performance by multi-model adaptation learning, and (3) the algorithm implementation and convergence theorem are also given. A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition. In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test data need to be independently identically distributed (IID); any individual user may show a completely different encephalogram (EEG) data in the same situation. The EEG data may be non-IID. In addition, noise/outlier sensitiveness still exist in GSSL approaches. To these ends, we propose in this paper a novel clustering method based on structure risk minimization model, called multi-model adaptation learning with possibilistic clustering assumption for EEG-based emotion recognition (MA-PCA). It can effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some reproduced kernel Hilbert space. Our main ideas are as follows: (1) reducing the negative impact of noise/outlier patterns through fuzzy entropy regularization, (2) considering the training data and test data are IID and non-IID to obtain a better performance by multi-model adaptation learning, and (3) the algorithm implementation and convergence theorem are also given. A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition.In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test data need to be independently identically distributed (IID); any individual user may show a completely different encephalogram (EEG) data in the same situation. The EEG data may be non-IID. In addition, noise/outlier sensitiveness still exist in GSSL approaches. To these ends, we propose in this paper a novel clustering method based on structure risk minimization model, called multi-model adaptation learning with possibilistic clustering assumption for EEG-based emotion recognition (MA-PCA). It can effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some reproduced kernel Hilbert space. Our main ideas are as follows: (1) reducing the negative impact of noise/outlier patterns through fuzzy entropy regularization, (2) considering the training data and test data are IID and non-IID to obtain a better performance by multi-model adaptation learning, and (3) the algorithm implementation and convergence theorem are also given. A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, one of the reasons affecting the performance of GSSL method is that the training data and test data need to be independently identically distribution (IID), each individual subject may present completely different encephalogram(EEG) patterns in the same scenario that result in the data will be non-IID. In addition, there has limited effort has been made on improving GSSL’s performance by reducing the influence of noise/outlier EEG-based patterns. To this end, we propose in this paper a novel clustering method based on structure risk minimization model, called a Multi-model adaptation method of possibilistic clustering assumption (MA-PCA) effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some Reproduced Kernel Hilbert Space. Its main ideas are as follows: (1) reducing the negative influence of noise/outlier patterns for the method through fuzzy entropy regularization; (2) considering the training data and test data at IID and non-IID by exploiting the proposed multi-mode adaptive learning, and then obtain a better performance; (3) the algorithm implementation and convergence theorem also are given. A large number of experiments and analysis deeply on multiple real datasets (i.e., DEAP, SEED and SEED-IV) show that the proposed method has superior or comparable robustness and generalization performance of the EEG-based emotion recognition. |
| Author | Zhou, Di Tao, Jianwen Dan, Yufang |
| AuthorAffiliation | 1 Institute of Artificial Intelligence Application, Ningbo Polytechnic , Ningbo , China 2 Key Laboratory of 3D Printing Equipment and Manufacturing in Colleges and Universities of Fujian Province , Fujian , China 3 Industrial Technological Institute of Intelligent Manufacturing, Sichuan University of Arts and Science , Dazhou , China |
| AuthorAffiliation_xml | – name: 1 Institute of Artificial Intelligence Application, Ningbo Polytechnic , Ningbo , China – name: 2 Key Laboratory of 3D Printing Equipment and Manufacturing in Colleges and Universities of Fujian Province , Fujian , China – name: 3 Industrial Technological Institute of Intelligent Manufacturing, Sichuan University of Arts and Science , Dazhou , China |
| Author_xml | – sequence: 1 givenname: Yufang surname: Dan fullname: Dan, Yufang – sequence: 2 givenname: Jianwen surname: Tao fullname: Tao, Jianwen – sequence: 3 givenname: Di surname: Zhou fullname: Zhou, Di |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35600616$$D View this record in MEDLINE/PubMed |
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| Keywords | encephalogram fuzzy entropy semi-supervised learning multi-model adaptation clustering assumption emotion recognition |
| Language | English |
| License | Copyright © 2022 Dan, Tao and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience Edited by: Yuanpeng Zhang, Nantong University, China These authors have contributed equally to this work Reviewed by: Liang Yu, Shanghai Jiao Tong University, China; Tingyang Chen, Wuhan University of Technology, China |
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| Title | Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition |
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