Exact entropy series representation for blind source separation

Author

Salam, F.M. ; Erten, G.

Author_Institution

Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA

Volume

1

fYear

1999

fDate

1999

Firstpage

553

Abstract

An explicit infinite series for the marginal entropy of a probability density function is developed. The series includes all orders of statistics and employs both the Gram-Charlier and the Edgeworth expansions for its derivation. The derivation exploits the fact that the two expansions are equivalent for the same probability density. The developed entropy series expression can be used to express the averaged mutual information to any degree of accuracy. This measure is then used in the derivation of the update laws of the blind separation of sources

Keywords

decorrelation; entropy; series (mathematics); signal reconstruction; signal representation; Edgeworth expansion; Gram-Charlier expression; averaged mutual information; blind source separation; exact entropy series representation; explicit infinite series; independent component analysis; marginal entropy; nonlinear signal processing; probability density function; Blind source separation; Density functional theory; Entropy; Filtering; Independent component analysis; Mutual information; Neural networks; Probability density function; Signal processing; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.814152

Filename

814152