Independent vector analysis by entropy rate bound minimization

被引：0

作者：

Fu, Geng-Shen ^{[1
]}

Anderson, Matthew ^{[1
]}

Adali, Tulay ^{[1
]}

机构：

[1] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA

来源：

2015 49th Annual Conference on Information Sciences and Systems (CISS) | 2015年

关键词：

Independent vector analysis; Mutual information rate; Entropy rate; vector autoregressive model; Maximum entropy distributions; BLIND SOURCE SEPARATION; JOINT DIAGONALIZATION; COMPONENT; IVA;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An extension of independent component analysis from one to multiple datasets, independent vector analysis, has recently become a subject of significant research interest. Since in many applications, latent sources are non-Gaussian, have sample dependence, and have dependence across multiple data sets, it is desirable to exploit all these properties jointly. Mutual information rate, which leads to the minimization of entropy rate, provides a natural cost for the task. In this paper, we present a new algorithm by using an effective entropy rate estimator, which takes all these properties into account. Experimental results show that the new method accounts for these properties effectively.

引用

页数：6