Discriminant independent component analysis

Authors:
Chandra Shekhar Dhir;Soo Young Lee
Affiliations:
Department of Bio and Brain Engineering, KAIST, Daejeon, Korea and Brain Science Research Center, KAIST, Daejeon, Korea;Dept. of Bio and Brain Eng., KAIST, Daejeon, Korea and Department of Electrical Eng. and Computer Science, KAIST, Daejeon, Korea and Brain Science Research Center, KAIST, Daejeon, Korea and RIKEN ...
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

In this paper, a new approach for extraction of discriminative and independent features is proposed. The proposed discriminant ICA (dICA) method jointly maximizes the inter-class variance and Negentropy of a given feature. Experimental results shows much improved classification performance when dICA features are used for recognition tasks over conventional ICA features. Moreover, dICA features show higher Fisher criterion score value suggesting a better capability to do class discrimination.