Pairwise Constraint Propagation-Induced Symmetric Nonnegative Matrix Factorization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)6348-6361
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Online published18 May 2018
Publication statusPublished - Dec 2018


As a variant of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) has shown to be effective for capturing the cluster structure embedded in the graph representation. In contrast to the existing SNMF-based clustering methods that empirically construct the similarity matrix and rigidly introduce the supervisory information to the assignment matrix, in this paper, we propose a novel SNMF-based semisupervised clustering method, namely, pairwise constraint propagation-induced SNMF (PCPSNMF). By formulating a single-constrained optimization problem, PCPSNMF is capable of learning the similarity and assignment matrices adaptively and simultaneously, in which a small amount of supervisory information in the form of pairwise constraints is introduced in a flexible way to guide the construction of the similarity matrix, and the two matrices communicate with each other to achieve mutual refinement until convergence. In addition, we propose an efficient alternating iterative algorithm to solve the optimization problem, whose convergence is theoretically proven. Experimental results over several benchmark image data sets demonstrate that PCPSNMF is less sensitive to initialization and produces higher clustering performance, compared with the state-of-the-art methods.

Research Area(s)

  • Clustering algorithms, Clustering methods, Convergence, Iterative methods, Kernel, Optimization, Pairwise constraint propagation (PCP), semisupervised clustering, Symmetric matrices, symmetric nonnegative matrix factorization (SNMF).