Efficient source adaptivity in independent component analysis

Author

Vlassis, Nikos ; Motomura, Yoichi

Author_Institution

Dept. of Comput. Sci., Amsterdam Univ., Netherlands

Volume

12

Issue

3

fYear

2001

fDate

5/1/2001 12:00:00 AM

Firstpage

559

Lastpage

566

Abstract

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve “source adaptivity” by directly estimating from the data the “true” score functions of the sources. We describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982). We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function

Keywords

maximum likelihood estimation; signal processing; statistical analysis; contrast function; efficient source adaptivity; fast density estimation method; global minimum; independent component analysis; score functions; source adaptivity; unknown sources; Algorithm design and analysis; Blind source separation; Independent component analysis; Maximum likelihood estimation; Microphones; Optimization methods; Parametric statistics; Source separation; Statistical analysis; Statistical distributions;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.925558

Filename

925558