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Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint

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conference contribution
posted on 2010-01-01, 00:00 authored by Zuyuan Yang, G Zhou, S Ding, S Xie
Recently, nonnegative matrix factorization (NMF) attracts more and more attentions for the promising of wide applications. A problem that still remains is that, however, the factors resulted from it may not necessarily be realistically interpretable. Some constraints are usually added to the standard NMF to generate such interpretive results. In this paper, a minimum-volume constrained NMF is proposed and an efficient multiplicative update algorithm is developed based on the natural gradient optimization. The proposed method can be applied to the blind source separation (BSS) problem, a hot topic with many potential applications, especially if the sources are mutually dependent. Simulation results of BSS for images show the superiority of the proposed method.

History

Event

Intelligent Control and Information Processing. Conference (2010 : Dalian, China)

Pagination

117 - 119

Publisher

IEEE Xplore

Location

Dalian, China

Place of publication

Piscataway, New Jersey

Start date

2010-08-13

End date

2010-08-15

ISBN-13

9781424470471

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2010, IEEE

Title of proceedings

ICICIP 2010 : Proceedings of the International Conference on Intelligent Control and Information Processing 2010

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