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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptation
verfasst von : Paresh Chandra Barman, Soo-Young Lee
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Verlag: Springer Berlin Heidelberg
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We proposed a novel algorithm of supervised feature selection and adaptation for enhancing the classification accuracy of unsupervised Nonnegative Matrix Factorization (NMF) feature extraction algorithm. At first the algorithm extracts feature vectors for a given high dimensional data then reduce the feature dimension using mutual information based relevant feature selection and finally adapt the selected NMF features using the proposed Non-negative Supervised Feature Adaptation (NSFA) learning algorithm. The supervised feature selection and adaptation improve the classification performance which is fully confirmed by simulations with text-document classification problem. Moreover, the non-negativity constraint, of this algorithm, provides biologically plausible and meaningful feature.