Paper Info
Title
Adaptive blind separation of underdetermined mixtures based on sparse component analysis.
Abstract
The independence priori is very often used in the conventional blind source separation (BSS). Naturally, independent component analysis (ICA) is also employed to perform BSS very often. However, ICA is difficult to use in some challenging cases, such as underdetermined BSS or blind separation of dependent sources. Recently, sparse component analysis (SCA) has attained much attention because it is theoretically available for underdetermined BSS and even for blind dependent source separation sometimes. However, SCA has not been developed very sufficiently. Up to now, there are only few existing algorithms and they are also not perfect as well in practice. For example, although Lewicki-Sejnowski’s natural gradient for SCA is superior to K-mean clustering, it is just an approximation without rigorously theoretical basis. To overcome these problems, a new natural gradient formula is proposed in this paper. This formula is derived directly from the cost function of SCA through matrix theory. Mathematically, it is more rigorous. In addition, a new and robust adaptive BSS algorithm is developed based on the new natural gradient. Simulations illustrate that this natural gradient formula is more robust and reliable than Lewicki-Sejnowski’s gradient.
Year
DOI
Venue
2008
10.1007/s11432-008-0030-4
Science in China Series F: Information Sciences
Keywords
Field
DocType
matrix theory,blind source separation,independent component analysis,cost function,k means clustering
Pattern recognition,Underdetermined system,Matrix (mathematics),Sparse approximation,Dependent source,Independent component analysis,Artificial intelligence,Component analysis,Cluster analysis,Blind signal separation,Mathematics
Journal
Volume
Issue
ISSN
51
4
18622836
Citations 
PageRank 
References 
1
0.35
19
Authors
4
Name
Order
Citations
PageRank
Zu-yuan Yang131224.12
Zhaoshui He235424.10
Shengli Xie32530161.51
Yuli Fu420029.90