A New Non-Orthogonal Joint Diagonalization Algorithm with Application in ICA and BSS

Author

Fuxiang, Wang ; Chongkan, Liu ; Jun, Zhang

Author_Institution

Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing

Volume

1

fYear

2006

fDate

4-6 Oct. 2006

Firstpage

89

Lastpage

92

Abstract

Joint diagonalization is an important family of methods for ICA (independent component analysis) and BSS (blind source separation). In this paper, we present a new approach to joint diagonalization of a set of 2x2 real symmetric matrices with a general (not necessarily orthogonal) matrix. It is a non-iterative algorithm in which the joint diagonalization problem can be solved by a special eigenvector problem. Some blind source separation (BSS) simulations results demonstrate that the algorithm has a substantial improvement in the separating performance.

Keywords

blind source separation; eigenvalues and eigenfunctions; independent component analysis; matrix algebra; BSS; ICA; blind source separation; eigenvector problem; independent component analysis; noniterative algorithm; nonorthogonal joint diagonalization algorithm; symmetric matrices; Additive noise; Blind source separation; Computational Intelligence Society; Cost function; Data models; Independent component analysis; Source separation; Statistics; Symmetric matrices; Systems engineering and theory; Blind source separation; independent component analysis; joint diagonalization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Engineering in Systems Applications, IMACS Multiconference on

Conference_Location

Beijing

Print_ISBN

7-302-13922-9

Electronic_ISBN

7-900718-14-1

Type

conf

DOI

10.1109/CESA.2006.4281629

Filename

4281629