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. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Barman, P. C., & Lee, S. Y. (2008). Nonnegative Matrix Factorization (NMF) based supervised feature selection and adaptation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5326 LNCS, pp. 120–127). Springer Verlag. https://doi.org/10.1007/978-3-540-88906-9_16
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