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Multi-view clustering by exploring complex mapping relationship between views
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DOI：10.1016/j.patrec.2020.07.031.png)
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Almost all of the existing methods assume that the samples between different views have a strict oneto-one relationship whether it is for complete multi-view data or for partial multi-view data. In this paper, we refer to the neglected many-to-many relationship between cross-view samples as the complex mapping relationship between views. To address this issue, we propose a resultful Complex Mapping Multi-View Clustering (CMMVC) method by exploring the complex mapping relationship between views. We firstly construct a complex mapping relationship matrix for each pair of views by using the nearest neighbor relationship between cross-view samples. Then the complex mapping relationship matrix is introduced into the framework of multi-view clustering based on non-negative matrix factorization to guide multi-view information fusion in order to obtain more compact representation of multi-view data space. Finally, we give the objective function of CMMVC and an effective optimization scheme. The experimental results demonstrate the advantages of the proposed CMMVC method on multi-view clustering tasks by mining the complex mapping relationship between different views. (C) 2020 Published by Elsevier B.V.
Keyword:
Multi-view clustering
Complex mapping relationship
Partial multi-view clustering
Non-negative matrix factorization (NMF)
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