Projected cross-view learning for unbalanced incomplete multi-view clustering

Cai, Y; Che, H; Pan, B; Leung, MF; Liu, C; Wen, S

Projected cross-view learning for unbalanced incomplete multi-view clustering

Cai, Y Che, H Pan, B Leung, MF Liu, C Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Fusion, 2024, 105, pp. 102245
Issue Date:: 2024-05-01

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Field	Value	Language
dc.contributor.author	Cai, Y
dc.contributor.author	Che, H
dc.contributor.author	Pan, B
dc.contributor.author	Leung, MF
dc.contributor.author	Liu, C
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-03-07T04:26:32Z
dc.date.available	2024-03-07T04:26:32Z
dc.date.issued	2024-05-01
dc.identifier.citation	Information Fusion, 2024, 105, pp. 102245
dc.identifier.issn	1566-2535
dc.identifier.uri	http://hdl.handle.net/10453/176261
dc.description.abstract	Incomplete multi-view clustering (IMVC) aims to partition samples into different groups for datasets with missing samples. The primary goal of IMVC is to effectively address the challenge posed by missing information in clustering analysis. Most existing IMVC methods focus on balanced incomplete multi-view data, assuming a uniform missing rate across all views. However, this assumption does not accurately reflect real-life scenarios. In reality, unbalanced incomplete multi-view data, characterized by varying missing rates among different views, is more prevalent. This presents significant challenges to the clustering process, as varying missing rates can lead to information imbalance. To address these challenges, this paper introduces a novel approach called projected cross-view learning for unbalanced incomplete multi-view clustering (PCL_UIMVC). Specifically, a reconstruction term is integrated, which leverages the information from the existing samples to facilitate the completion of the unbalanced incomplete multi-view data. Next, a projection matrix is incorporated into the model to harmonize feature dimensions across views, mitigating the impact of information imbalance. Then, a graph regularization term is integrated to preserve the geometric structure of the original data. Finally, an iterative algorithm is developed to solve the proposed model. Extensive experiments on eight standard datasets, featuring various rates of missing data, validate the superior clustering performance of the proposed method.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Fusion
dc.relation.isbasedon	10.1016/j.inffus.2024.102245
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4605 Data management and data science
dc.title	Projected cross-view learning for unbalanced incomplete multi-view clustering
dc.type	Journal Article
utslib.citation.volume	105
utslib.for	0801 Artificial Intelligence and Image Processing
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	2024-03-07T04:26:30Z
pubs.publication-status	Accepted
pubs.volume	105

Abstract:

Incomplete multi-view clustering (IMVC) aims to partition samples into different groups for datasets with missing samples. The primary goal of IMVC is to effectively address the challenge posed by missing information in clustering analysis. Most existing IMVC methods focus on balanced incomplete multi-view data, assuming a uniform missing rate across all views. However, this assumption does not accurately reflect real-life scenarios. In reality, unbalanced incomplete multi-view data, characterized by varying missing rates among different views, is more prevalent. This presents significant challenges to the clustering process, as varying missing rates can lead to information imbalance. To address these challenges, this paper introduces a novel approach called projected cross-view learning for unbalanced incomplete multi-view clustering (PCL_UIMVC). Specifically, a reconstruction term is integrated, which leverages the information from the existing samples to facilitate the completion of the unbalanced incomplete multi-view data. Next, a projection matrix is incorporated into the model to harmonize feature dimensions across views, mitigating the impact of information imbalance. Then, a graph regularization term is integrated to preserve the geometric structure of the original data. Finally, an iterative algorithm is developed to solve the proposed model. Extensive experiments on eight standard datasets, featuring various rates of missing data, validate the superior clustering performance of the proposed method.

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http://hdl.handle.net/10453/176261