Generative and discriminant feature extraction with supervised learning

Chandra S. Dhir; Soo-Young Lee

doi:10.1117/12.883260

14 June 2011 Generative and discriminant feature extraction with supervised learning

Chandra S. Dhir, Soo-Young Lee

Proceedings Volume 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX; 80580I (2011) https://doi.org/10.1117/12.883260
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

Chandra S. Dhir and Soo-Young Lee "Generative and discriminant feature extraction with supervised learning", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580I (14 June 2011); https://doi.org/10.1117/12.883260

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