A post nonlinear geometric algorithm for independent component analysis
Nguyen, Thang Viet
Patra, Jagdish Chandra
Date of Issue2005
School of Computer Engineering
Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing environments. Whereas, a nonlinear ICA model, which is more complicated, would be more practical for general applications as it can work with both linear and nonlinear mixtures. In this paper, we introduce a novel method for nonlinear ICA problem. The proposed method follows the post nonlinear approach to model the mixtures, and exploits the difference between a linear mixture and a nonlinear one from their nature of distributions in a multidimensional space to develop a separation scheme. The nonlinear mixture is represented by a nonlinear surface while the linear mixture is represented by a plane. A geometric learning algorithm named as post nonlinear geometric ICA (pnGICA) is developed by geometrically transforming the nonlinear surface to a plane, i.e., to a linear mixture. Computer simulations of the algorithm provide promising performance on different data sets.
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Digital signal processing
© 2005 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Digital Signal Processing, Elsevier. It incorporates referee's comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI: http://dx.doi.org/10.1016/j.dsp.2004.12.006].