DC Field | Value | Language |
---|---|---|
dc.contributor.author | Barman, Paresh Chandra | - |
dc.contributor.author | Lee, Soo-Young | - |
dc.date.accessioned | 2009-06-25T02:25:07Z | - |
dc.date.available | 2009-06-25T02:25:07Z | - |
dc.date.issued | 2008-11 | - |
dc.identifier.citation | Lecture Notes in Computer Science, Vol.5326, pp.120-127 | en |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10203/9734 | - |
dc.description.abstract | 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. | en |
dc.description.sponsorship | This work was supported as the Brain Neuroinformatics Research Program by Korean Ministry of Commerce, Industry and Energy. | en |
dc.language.iso | en_US | en |
dc.publisher | Springer Verlag (Germany) | en |
dc.subject | Nonnegative Matrix Factorization | en |
dc.subject | Feature Adaptation | en |
dc.subject | Feature extraction | en |
dc.subject | Feature selection | en |
dc.subject | Document classification | en |
dc.title | Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptation | en |
dc.type | Article | en |
dc.identifier.doi | 10.1007/978-3-540-88906-9 | - |
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