Multi-view clustering via multi-manifold regularized non-negative matrix factorization

Non-negative matrix factorization based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, non-negative matrix factorization fails to preserve the locally geometrical structure of the data space. In this paper, we propose a mu...

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Vydáno v:	Neural networks Ročník 88; s. 74 - 89
Hlavní autoři:	Zong, Linlin, Zhang, Xianchao, Zhao, Long, Yu, Hong, Zhao, Qianli
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.04.2017
Témata:	Algorithms Cluster Analysis Locally linear embedding (LLE) Multi-manifold Multi-view clustering Non-negative matrix factorization Locally linear embedding (LLE) Multi-view clustering Non-negative matrix factorization Multi-manifold
ISSN:	0893-6080, 1879-2782, 1879-2782
On-line přístup:	Získat plný text
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Shrnutí:	Non-negative matrix factorization based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, non-negative matrix factorization fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized non-negative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF incorporates consensus manifold and consensus coefficient matrix with multi-manifold regularization to preserve the locally geometrical structure of the multi-view data space. We use two methods to construct the consensus manifold and two methods to find the consensus coefficient matrix, which leads to four instances of the framework. Experimental results show that the proposed algorithms outperform existing non-negative matrix factorization based algorithms for multi-view clustering.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2017.02.003