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LEARNING SPARSE MIXTURE MODELS FOR DISCRIMINATIVE CLASSIFICATION.
- Source :
-
International Journal of Pattern Recognition & Artificial Intelligence . May2006, Vol. 20 Issue 3, p431-440. 10p. 2 Charts. - Publication Year :
- 2006
-
Abstract
- Recently Saul and Lee proposed a mixture model for discriminative classification of non-negative data via non-negative matrix factorization for feature extraction. In order to improve the generalization, this paper considers a sparse version of the model. The basic idea is to minimize the sum of the weights of un-normalized mixture models for posterior distributions according to regularization method. Experiments on CBCL face database and USPS digit data set assess the validity of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 20
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 20900366
- Full Text :
- https://doi.org/10.1142/S0218001406004752