An ICA Learning Algorithm Utilizing Geodesic Approach

This paper presents a novel independent component analysis algorithm that separates mixtures using serially updating geodesic method. The geodesic method is derived from the Stiefel manifold, and an on-line version of this method that can directly treat with the unwhitened observations is obtained....

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Bibliographic Details
Published inAdvances in Neural Networks - ISNN 2006 pp. 1103 - 1108
Main Authors Yu, Tao, Shao, Huai-Zong, Peng, Qi-Cong
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Blind Source Separation
Computer science; control theory; systems
Exact sciences and technology
Geodesic Flow
Independent Component Analysis
Independent Component Analysis Algorithm
Instantaneous Mixture
Independent component analysis
Geodesic
Method of lines
Updating
Geodesic structure
Neural network
Learning algorithm
Bioinformatics
Artificial intelligence
Numerical convergence
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Summary:This paper presents a novel independent component analysis algorithm that separates mixtures using serially updating geodesic method. The geodesic method is derived from the Stiefel manifold, and an on-line version of this method that can directly treat with the unwhitened observations is obtained. Simulation of artificial data as well as real biological data reveals that our proposed method has fast convergence.
ISBN:354034439X
9783540344391
ISSN:0302-9743
1611-3349
DOI:10.1007/11759966_162