Between/Within View Information Completing for Tensorial Incomplete Multi-view Clustering

Incomplete Multi-view Clustering (IMvC) receives increasing attention due to its effectiveness in solving data-missing problems. With the information loss in incomplete situations, the core of IMvC needs to consider effectively overcoming the challenge of missing views, that is, exploring the underl...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2024-12, p.1-13
Main Authors: Yao, Mingze, Wang, Huibing, Chen, Yawei, Fu, Xianping
Format: Article
Language:English
Subjects:
Between/within view information
Bipartite graph
Clustering algorithms
Clustering methods
Correlation
dual tensor constraint
Fast Fourier transforms
Incomplete Multi-view Clustering (IMvC)
Kernel
Perturbation methods
Tensors
Vectors
Web pages
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Summary:Incomplete Multi-view Clustering (IMvC) receives increasing attention due to its effectiveness in solving data-missing problems. With the information loss in incomplete situations, the core of IMvC needs to consider effectively overcoming the challenge of missing views, that is, exploring the underlying correlations from available data and recovering the missing information. However, most existing IMvC methods overemphasize the recovery-first principle with integrating the existing data from different views while neglecting the influence of view consistency in IMvC task together with valuable within view information. In this paper, a novel Between/Within View Information Completing for Tensorial Incomplete Multi-view Clustering (BWIC-TIMC) has been proposed, in which between/within view information is jointly exploited for effectively completing the missing views. Specifically, the proposed method designs a dual tensor constraint module, which focuses on simultaneously exploring the view-specific correlations of incomplete views and enforcing the between view consistency across different views. With the dual tensor constraint, between/within view information can be effectively integrated for completing missing views for IMvC task. Furthermore, in order to balance different contributions of multiple views and alleviate the problem of feature degeneration, BWIC-TIMC implements an adaptive fusion graph learning strategy for consensus representation learning. Extensive comparative experiments with the-state-of-art baselines can demonstrate the effectiveness of BWIC-TIMC.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2024.3521771