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Proceedings Paper

Generative and discriminant feature extraction with supervised learning
Author(s): Chandra S. Dhir; Soo-Young Lee
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Paper Abstract

Standard unsupervised feature extraction methods such as PCA and ICA provide representative features and latent variables which minimizes the data reconstruction error. These generative features may be common to all data, and may not be optimal for classification tasks. The discriminate ICA (dICA) and discriminant NMF (dNMF) had recently been proposed which jointly maximizes Fisher linear discriminant and Negentropy of the extracted features. Motivated by independence among features and modified Fisher linear discriminant, the new algorithm extracts features with both generative and discriminant powers. Then, the features are further fine-tuned by supervised learning. Experimental results show excellent recognition performance with these features.

Paper Details

Date Published: 14 June 2011
PDF: 4 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580I (14 June 2011); doi: 10.1117/12.883260
Show Author Affiliations
Chandra S. Dhir, KAIST (Korea, Republic of)
Soo-Young Lee, KAIST (Korea, Republic of)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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