Robust volume minimization-based matrix factorization via alternating optimization

Xiao Fu, Wing Kin Ma, Kejun Huang, Nicholas D. Sidiropoulos

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

5 Scopus citations

Abstract

This paper focuses on volume minimization (VolMin)-based structured matrix factorization (SMF), which factors a data matrix into a full-column rank basis and a coefficient matrix whose columns reside in the unit simplex. The VolMin criterion achieves this goal via finding a minimum-volume enclosing convex hull of the data. Recent works showed that VolMin guarantees the identifiability of the factor matrices under mild and realistic conditions, which suit many applications in signal processing and machine learning. However, the existing VolMin algorithms are sensitive to outliers or lack efficiency in dealing with volume-associated cost functions. In this work, we propose a new VolMin-based matrix factorization criterion and algorithm that take outliers into consideration. The proposed algorithm detects outliers and suppress them automatically, and it does so in an algorithmically very simple way. Simulations are used to showcase the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2534-2538
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • Volume minimization
  • document clustering
  • hyperspectral unmixing
  • nonnegative matrix factorization

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