Title :
On nonlinear independent component analysis using self-organizing map
Author :
Wang, Gang ; Hu, Dewen
Author_Institution :
Coll. of Mechatronics & Autom., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
Nonlinear independent component analysis (NICA) is a generalization of basic independent component analysis (ICA), and many methods have been proposed to recover signals from nonlinear mixtures. The work is performed on a post-nonlinear model, and the emphasis is focused on the method of self-organizing map (SOM). Two issues are addressed, (1) factor rotation is a crucial step in the process of inverse transform, and (2) density distortion is introduced by post-nonlinear transform. As SOM is a homeomorphism from input space to the maps, it cannot perform factor rotation and remove distortion. Experiments show that the rough nonlinear separation ability comes mostly from the decorrelation via whitening rather than from the SOM, and the process of NICA using SOM is in essence a second-order statistical method.
Keywords :
independent component analysis; inverse problems; nonlinear distortion; self-organising feature maps; source separation; transforms; ICA; density distortion; homeomorphism; inverse transform; nonlinear independent component analysis; nonlinear mixtures; post nonlinear model; post nonlinear transform; rough nonlinear separation; second order statistical method; self organizing map; signal recovery; Automation; Decorrelation; Educational institutions; Independent component analysis; Mechatronics; Nonlinear distortion; Predistortion; Radial basis function networks; Signal processing algorithms; Statistical analysis;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1340492