A New Constrained Independent Component Analysis Method

Author

Huang, De-Shuang ; Mi, Jian-Xun

Author_Institution

Chinese Acad. of Sci., Hefei

Volume

18

Issue

5

fYear

2007

Firstpage

1532

Lastpage

1535

Abstract

Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved algorithm for cICA is presented through the investigation of the inequality constraints, in which different closeness measurements are compared. The utility of our proposed algorithm is demonstrated by the experiments with synthetic data and electroencephalogram (EEG) data.

Keywords

electroencephalography; independent component analysis; augmented Lagrangian function; constrained independent component analysis method; electroencephalogram data; inequality constraints; negentropy contrast function; synthetic data; Biomedical measurements; Content addressable storage; Data mining; Electroencephalography; Independent component analysis; Information analysis; Laboratories; Lagrangian functions; Machine intelligence; Signal processing algorithms; Constrained optimization; ICA with reference (ICA-R); electroencephalogram (EEG); independent component analysis (ICA); Algorithms; Artificial Intelligence; Computer Simulation; Diagnosis, Computer-Assisted; Electroencephalography; Models, Neurological; Pattern Recognition, Automated; Principal Component Analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.895910

Filename

4298116