Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Li, C; Che, H; Leung, MF; Liu, C; Yan, Z

Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Li, C Che, H Leung, MF Liu, C Yan, Z

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2023, 634, pp. 587-607
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Li, C
dc.contributor.author	Che, H
dc.contributor.author	Leung, MF
dc.contributor.author	Liu, C
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.date.accessioned	2024-03-18T03:57:37Z
dc.date.available	2024-03-18T03:57:37Z
dc.date.issued	2023-07-01
dc.identifier.citation	Information Sciences, 2023, 634, pp. 587-607
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/176847
dc.description.abstract	Multi-view clustering (MVC) has received extensive attention due to its efficient processing of high-dimensional data. Most of the existing multi-view clustering methods are based on non-negative matrix factorization (NMF), which can achieve dimensionality reduction and interpretable representation. However, there are following issues in the existing researches: (1) The existing methods based on NMF using Frobenius norm are sensitive to noises and outliers. (2) Many methods only use the information shared by multi-view data, while ignoring the diverse information between views. (3) The data graph constructed by the conventional K Nearest Neighbors (KNN) method may misclassify neighbors and degrade the clustering performance. To address the above problems, we propose a novel robust multi-view clustering method. Specifically, l2,1-norm is introduced to measure the factorization error to improve the robustness of NMF. Additionally, a diversity constraint is utilized to learn the diverse relationship of multi-view data, and an adaptive graph method via information entropy is designed to overcome the shortcomings of misclassifying neighbors. Finally, an iterative updating algorithm is developed to solve the optimization model, which can make the objective function monotonically non-increasing. The effectiveness of the proposed method is substantiated by comparing with eleven state-of-the-art methods on five real-world and four synthetic multi-view datasets for clustering tasks.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2023.03.119
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints
dc.type	Journal Article
utslib.citation.volume	634
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	2024-03-18T03:57:31Z
pubs.publication-status	Published
pubs.volume	634

Abstract:

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of high-dimensional data. Most of the existing multi-view clustering methods are based on non-negative matrix factorization (NMF), which can achieve dimensionality reduction and interpretable representation. However, there are following issues in the existing researches: (1) The existing methods based on NMF using Frobenius norm are sensitive to noises and outliers. (2) Many methods only use the information shared by multi-view data, while ignoring the diverse information between views. (3) The data graph constructed by the conventional K Nearest Neighbors (KNN) method may misclassify neighbors and degrade the clustering performance. To address the above problems, we propose a novel robust multi-view clustering method. Specifically, l2,1-norm is introduced to measure the factorization error to improve the robustness of NMF. Additionally, a diversity constraint is utilized to learn the diverse relationship of multi-view data, and an adaptive graph method via information entropy is designed to overcome the shortcomings of misclassifying neighbors. Finally, an iterative updating algorithm is developed to solve the optimization model, which can make the objective function monotonically non-increasing. The effectiveness of the proposed method is substantiated by comparing with eleven state-of-the-art methods on five real-world and four synthetic multi-view datasets for clustering tasks.

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http://hdl.handle.net/10453/176847