title: Multi-view clustering via multi-manifold regularized non-negative matrix factorization.
abstract: 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.
authors.affiliation: Dalian University of Technology, Dalian, 116620, China. Electronic address: zonglinlin324@gmail.com.
authors.forename: Linlin
authors.initials: L
authors.lastname: Zong
authors.affiliation: Dalian University of Technology, Dalian, 116620, China. Electronic address: xczhang@dlut.edu.cn.
authors.forename: Xianchao
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Dalian University of Technology, Dalian, 116620, China. Electronic address: zhaolong.324@163.com.
authors.forename: Long
authors.initials: L
authors.lastname: Zhao
authors.affiliation: Dalian University of Technology, Dalian, 116620, China. Electronic address: hongyu@dlut.edu.cn.
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
authors.affiliation: Dalian University of Technology, Dalian, 116620, China. Electronic address: sconan32@live.com.
authors.forename: Qianli
authors.initials: Q
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis
keywords: Locally linear embedding (LLE); Multi-manifold; Multi-view clustering; Non-negative matrix factorization