Blind source separation - Semiparametric statistical approach

被引：177

作者：

Amari, S

Cardoso, JF

机构：

[1] ECOLE NATL SUPER TELECOMMUN BRETAGNE,CNRS,SIGNAL DEPT,PARIS,FRANCE

[2] UNIV TOKYO,BUNKYO KU,TOKYO 113,JAPAN

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 1997年 / 45卷 / 11期

关键词：

blind source separation; estimating function; independent component analysis; learning algorithm; semiparametric statistical model;

D O I：

10.1109/78.650095

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The semiparametric statistical model is used to formulate the problem of blind source separation. The method of estimating functions is applied to this problem, It is shown that an estimator of the mixing matrix or its learning version can be described in terms of an estimating function. The statistical efficiencies of these algorithms are studied. The main results are as follows. 1) The space consisting of all the estimating functions is derived. 2) The spare is decomposed into the orthogonal sum of the admissible part and redundant ancillary part. For any estimating function, one can find a better or equally good estimator in the admissible part. 3) The Fisher efficient (that is, asymptotically best) estimating functions are derived4) The stability of learning algorithms is studied.

引用

页码：2692 / 2700

页数：9