DocumentCode
3584546
Title
Natural gradient for temporally dependent component analysis
Author
Shi, Zhenwei ; Cheng, Dalong ; Tan, Xueyan ; Jiang, Zhiguo
Author_Institution
Image Process. Center, Beihang Univ., Beijing, China
Volume
2
fYear
2010
Firstpage
972
Lastpage
975
Abstract
The temporally dependent component analysis (TDCA) method for blind source separation (BSS) is introduced. As a new principle, it is shown that maximizing the mapping of autocorrelation of source signals can be used to perform BSS. We use the natural gradient algorithm for TDCA and study the mathematical properties of TDCA. Simulations by square temporal autocorrelation sources verify the efficient implementation of the proposed method.
Keywords
blind source separation; correlation methods; independent component analysis; blind source separation; natural gradient algorithm; source signals; square temporal autocorrelation sources; temporally dependent component analysis; Algorithm design and analysis; Blind source separation; Correlation; Independent component analysis; Signal processing algorithms; Stability analysis; Blind source separation (BSS); Independent component analysis (ICA); Linear autocorrelation; Nonlinear autocorrelation;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5583820
Filename
5583820
Link To Document