Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint
Yang, Zuyuan, Zhou, Guoxu, Ding, Shuxue and Xie, Shengli 2010, Blind Source Separation by Nonnegative Matrix Factorization with Minimum-Volume Constraint, in ICICIP 2010 : Proceedings of the International Conference on Intelligent Control and Information Processing 2010, IEEE Xplore, Piscataway, New Jersey, pp. 117-119, doi: 10.1109/ICICIP.2010.5565228.
Intelligent Control and Information Processing International Conference
Start page
117
End page
119
Total pages
3
Publisher
IEEE Xplore
Place of publication
Piscataway, New Jersey
Summary
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.
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