Principal independent component analysis

Author

Luo, Jie ; Hu, Bo ; Ling, Xie-Ting ; Liu, Ruey-wen

Author_Institution

Dept. of Electr. Eng., Notre Dame Univ., IN, USA

Volume

10

Issue

4

fYear

1999

fDate

7/1/1999 12:00:00 AM

Firstpage

912

Lastpage

917

Abstract

Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available. In this paper, a principal independent component analysis (PICA) concept is proposed. We try to extract the objective independent component directly without separating all the signals. A cumulant-based globally convergent algorithm is presented and simulation results are given to show the hopeful applicability of the PICA ideas

Keywords

convergence; higher order statistics; principal component analysis; signal processing; PICA; blind signal separation algorithms; convergence; cumulant-based globally convergent algorithm; objective independent component; principal independent component analysis; Blind source separation; Convergence; Data mining; Feature extraction; Independent component analysis; Principal component analysis; Signal detection; Signal processing; Signal processing algorithms; Statistical analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.774259

Filename

774259