Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptation

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.
Publisher
Springer Verlag (Germany)
Issue Date
2008-11
Keywords

Nonnegative Matrix Factorization; Feature Adaptation; Feature extraction; Feature selection; Document classification

Citation

Lecture Notes in Computer Science, Vol.5326, pp.120-127

ISSN
0302-9743
DOI
10.1007/978-3-540-88906-9
URI
http://hdl.handle.net/10203/9734
Appears in Collection
EE-Journal Papers(저널논문)
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