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Learning sparse features for classification by mixture models
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DOI：10.1016/j.patrec.2003.09.007.png)
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Non-negative matrix factorization (NMF) can discover sparse features for classification via mixture models and the sparseness of features controls the learning rate of the basis function parameters. But the original NMF in which the basis vectors are unit ones in L-1 norm, does not increase the sparseness of learned features. This paper generalizes NMF to L-p-NMF where the basis vectors are unit ones in L-p norm. Experiments demonstrate how p affects the sparseness of learned features and the final classification accuracy. And the results show that L-2-NMF is superior one for practical implementation. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
non-negative matrix factorization
classification
mixture models
sparse features
Lp norm
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3.3
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1.6W
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