Multi-View Consensus Proximity Learning for Clustering

Most proximity-based multi-view clustering methods are sensitive to the initial proximity matrix, where the clustering performance is quite unstable when using different initial proximity matrixes. This problem is defined as the initial value sensitivity problem. Since clustering is an unsupervised...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2022-07, Vol.34 (7), p.3405-3417
Main Authors: Liu, Bao-Yu, Huang, Ling, Wang, Chang-Dong, Lai, Jian-Huang, Yu, Philip S.
Format: Article
Language:English
Subjects:
Clustering
Clustering methods
Cognitive tasks
consensus proximity learning
data representative learning
Electronic mail
initial value stability
Iterative methods
Laplace equations
Learning systems
Multi-view clustering
Proximity
proximity-based clustering
self-weighted method
Sensitivity
Task analysis
Teaching methods
Unsupervised learning
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Summary:Most proximity-based multi-view clustering methods are sensitive to the initial proximity matrix, where the clustering performance is quite unstable when using different initial proximity matrixes. This problem is defined as the initial value sensitivity problem. Since clustering is an unsupervised learning task, it is unrealistic to tune the initial proximity matrix. Thus, how to overcome the initial value sensitivity problem is a significant but unsolved issue in the proximity-based multi-view clustering. To this end, this paper proposes a novel multi-view proximity learning method, named multi-view consensus proximity learning (MCPL). On the one hand, by integrating the information of all views in a self-weighted manner and giving a rank constraint on the Laplacian matrix, the MCPL method learns the consensus proximity matrix to directly reflect the clustering result. On the other hand, different from most multi-view proximity learning methods, in the proposed MCPL method, the data representatives rather than the original data objects are adopted to learn the consensus proximity matrix. The data representatives will be updated in the process of the proximity learning so as to weaken the impact of the initial value on the clustering performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.3025759