Minimum-volume-regularized weighted symmetric nonnegative matrix factorization for clustering

Tianxiang Gao, Sigurdur Olofsson, Songtao Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and outperforms NMF when dealing with nonlinear data structure. However, the clustering results of SNMF is very sensitive to noisy data. In this paper, we propose a minimum-volume-regularized weighted SNMF (MV-WSNMF) based on the relationship between robust NMF and SNMF. The proposed MV-WSNMF can approximate the similarity matrices flexibly such that the resulting performance is more robust against noise. A computationally efficient algorithm is also proposed with convergence guarantee. The numerical simulation results show the improvement of the proposed algorithm with respective to clustering accuracy in comparison with the state-of-the-art algorithms.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages247-251
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Clustering
  • Nonnegative matrix factorization (NMF)
  • Volume minimization

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