Robust volume minimization-based matrix factorization via alternating optimization
Refereed conference paper presented and published in conference proceedings


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AbstractThis 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.
All Author(s) ListFu X., Ma W.-K., Huang K., Sidiropoulos N.D.
Name of Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Start Date of Conference20/03/2016
End Date of Conference25/03/2016
Place of ConferenceShanghai
Country/Region of ConferenceChina
Detailed descriptionorganized by IEEE,
Year2016
Month5
Day18
Volume Number2016-May
Pages2534 - 2538
ISBN9781479999880
ISSN1520-6149
LanguagesEnglish-United Kingdom
Keywordsdocument clustering, hyperspectral unmixing, nonnegative matrix factorization, Volume minimization

Last updated on 2020-19-11 at 01:12