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- W1975569673 abstract "Standard unsupervised feature extraction methods such as PCA and ICA provide representative features and latentvariables which minimizes the data reconstruction error. These generative features may be common to all data, and maynot be optimal for classification tasks. The discriminate ICA (dICA) and discriminant NMF (dNMF) had recently beenproposed which jointly maximizes Fisher linear discriminant and Negentropy of the extracted features. Motivated byindependence among features and modified Fisher linear discriminant, the new algorithm extracts features with bothgenerative and discriminant powers. Then, the features are further fine-tuned by supervised learning. Experimentalresults show excellent recognition performance with these features." @default.
- W1975569673 created "2016-06-24" @default.
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- W1975569673 date "2011-05-13" @default.
- W1975569673 modified "2023-09-23" @default.
- W1975569673 title "Generative and discriminant feature extraction with supervised learning" @default.
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- W1975569673 doi "https://doi.org/10.1117/12.883260" @default.
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