论文成果
Multi-view clustering via multi-manifold regularized non-negative matrix factorization
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- 论文类型：期刊论文
- 发表时间：2017-04-01
- 发表刊物：NEURAL NETWORKS
- 收录刊物：SCIE、EI、PubMed、Scopus
- 文献类型：J
- 卷号：88
- 页面范围：74-89
- ISSN号：0893-6080
- 关键字：Non-negative matrix factorization; Multi-view clustering;
Multi-manifold; Locally linear embedding (LLE)
- 摘要：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. (C) 2017 Elsevier Ltd. All rights reserved.
