GpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization. (16th November 2006)

• Record Type: Journal Article
• Authors: Nguyen Nguyen, Thang Viet Thang Viet; Patra Patra, Jagdish Chandra Jagdish Chandra; Emmanuel Emmanuel, Sabu Sabu
• Other Names: Ehlers Ehlers Frank Frank Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-11-16
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/31951
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : A new geometric approach for nonlinear independent component analysis (ICA) is presented in this paper. Nonlinear environment is modeled by the popular post nonlinear (PNL) scheme. To eliminate the nonlinearity in the observed signals, a novel linearizing method named as geometric post nonlinear ICA (gpICA) is introduced. Thereafter, a basic linear ICA is applied on these linearized signals to estimate the unknown sources. The proposed method is motivated by the fact that in a multidimensional space, a nonlinear mixture is represented by a nonlinear surface while a linear mixture is represented by a plane, a special form of the surface. Therefore, by geometrically transforming the surface representing a nonlinear mixture into a plane, the mixture can be linearized. Through simulations on different data sets, superior performance of gpICA algorithm has been shown with respect to other algorithms.