Blind source separation-semiparametric statistical approach

Author

Amari, Shun-Ichi ; Cardoso, Jean-François

Author_Institution

RIKEN, Inst. of Phys. & Chem. Res., Saitama, Japan

Volume

45

Issue

11

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

2692

Lastpage

2700

Abstract

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 space is decomposed into the orthogonal sum of the admissible part and a 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 derived. (4) The stability of learning algorithms is studied

Keywords

functional analysis; learning systems; matrix algebra; parameter estimation; signal processing; statistical analysis; Fisher efficient estimating function; admissible part; blind source separation; estimating function method; learning algorithms stability; learning version; mixing matrix; redundant ancillary part; semiparametric statistical model; statistical efficiencies; Asymptotic stability; Blind source separation; Convergence; Covariance matrix; Independent component analysis; Joining processes; Matrix decomposition; Probability distribution; Proposals; Signal processing algorithms;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.650095

Filename

650095