Convergence Analysis of Non-Negative Matrix Factorization for BSS Algorithm

被引：0
|
作者：
Shangming Yang
Zhang Yi
机构：
[1] University of Electronic Science and Technology of China,School of Computer Science and Engineering
[2] Sichuan University,College of Computer Science
来源：
Neural Processing Letters | 2010年 / 31卷
关键词：
BSS; Convergence analysis; Non-negative ICA; Non-negative matrix factorization; NMF; KL divergence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper the convergence of a recently proposed BSS algorithm is analyzed. This algorithm utilized Kullback–Leibler divergence to generate non-negative matrix factorizations of the observation vectors, which is considered an important aspect of the BSS algorithm. In the analysis some invariant sets are constructed so that the convergence of the algorithm can be guaranteed in the given conditions. In the simulation we successfully applied the algorithm and its analysis results to the blind source separation of mixed images and signals.
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页码：45 / 64
页数：19
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