Sparse component analysis and blind source separation of underdetermined mixtures

被引：265

作者：

Georgiev, P ^{[1
]}

Theis, F

Cichocki, A

机构：

[1] Univ Cincinnati, ECECS Dept, Cincinnati, OH 45221 USA

[2] Univ Regensburg, Inst Biophys, D-93040 Regensburg, Germany

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 04期

关键词：

blind source separation (BSS); sparse component analysis (SCA); underdetermined mixtures;

D O I：

10.1109/TNN.2005.849840

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this letter, we solve the problem of identifying matrices S is an element of R-n x N and A is an element of R-m x n knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.

引用

页码：992 / 996

页数：5

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