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.
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Dhir, C.S., Lee, S.Y. (2009). Discriminant Independent Component Analysis. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_27
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DOI: https://doi.org/10.1007/978-3-642-04394-9_27
Publisher Name: Springer, Berlin, Heidelberg
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