Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis

Shi, M; Zhao, X; Yin, X; Chang, X; Niu, F; Guo, J

Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis

Shi, M Zhao, X Yin, X Chang, X

Niu, F Guo, J

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2023, 276
Issue Date:: 2023-09-27

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Field	Value	Language
dc.contributor.author	Shi, M
dc.contributor.author	Zhao, X
dc.contributor.author	Yin, X
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Niu, F
dc.contributor.author	Guo, J
dc.date.accessioned	2024-02-07T05:00:04Z
dc.date.available	2024-02-07T05:00:04Z
dc.date.issued	2023-09-27
dc.identifier.citation	Knowledge-Based Systems, 2023, 276
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/175427
dc.description.abstract	Multiview subspace learning (MvSL) has attracted extensive research interest in many real-world applications, e.g., pattern recognition and computer vision. Existing MvSL approaches seek a discriminant common subspace by simultaneously maximizing the arithmetic mean of the between-class covariance and minimizing the global average of the within-class covariance. However, the arithmetic mean normally assigns equal weight to all between-class distances. Then, classes with small distances overlap with each other, and the largest between-class distance dominates the common subspace. In addition, the global average of within-class covariance (which is computed by the sum of within-class distances for all classes) significantly differs from the within-class covariance of a single class. This will inevitably cause the internal details of each class to be ignored and lead to lower recognition accuracy. To circumvent these issues, we develop a productive strategy to take more consideration of the relationship between any two classes and propose a novel Multiview Latent Structure Learning (MvLSL) model, which incorporates the weighted harmonic mean of pairwise between-class scatter and pairwise within-class scatter. Due to the adjustable dimensions of the common subspace, MvLSL can be naturally extended for dimensionality reduction (DR), and we utilize a reconstruction term to suppress data degradation and improve the DR performance. Our models are tested on six benchmark datasets with five evaluation criteria and compared with existing state-of-the-art methods, indicating our models’ effectiveness and superiority.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2023.110707
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4605 Data management and data science
dc.subject.classification	4611 Machine learning
dc.title	Multiview Latent Structure Learning: Local structure-guided cross-view discriminant analysis
dc.type	Journal Article
utslib.citation.volume	276
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-07T05:00:03Z
pubs.publication-status	Published
pubs.volume	276

Abstract:

Multiview subspace learning (MvSL) has attracted extensive research interest in many real-world applications, e.g., pattern recognition and computer vision. Existing MvSL approaches seek a discriminant common subspace by simultaneously maximizing the arithmetic mean of the between-class covariance and minimizing the global average of the within-class covariance. However, the arithmetic mean normally assigns equal weight to all between-class distances. Then, classes with small distances overlap with each other, and the largest between-class distance dominates the common subspace. In addition, the global average of within-class covariance (which is computed by the sum of within-class distances for all classes) significantly differs from the within-class covariance of a single class. This will inevitably cause the internal details of each class to be ignored and lead to lower recognition accuracy. To circumvent these issues, we develop a productive strategy to take more consideration of the relationship between any two classes and propose a novel Multiview Latent Structure Learning (MvLSL) model, which incorporates the weighted harmonic mean of pairwise between-class scatter and pairwise within-class scatter. Due to the adjustable dimensions of the common subspace, MvLSL can be naturally extended for dimensionality reduction (DR), and we utilize a reconstruction term to suppress data degradation and improve the DR performance. Our models are tested on six benchmark datasets with five evaluation criteria and compared with existing state-of-the-art methods, indicating our models’ effectiveness and superiority.

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http://hdl.handle.net/10453/175427