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Push–Pull marginal discriminant analysis for feature extraction
- Source :
-
Pattern Recognition Letters . Nov2010, Vol. 31 Issue 15, p2345-2352. 8p. - Publication Year :
- 2010
-
Abstract
- Abstract: Marginal information is of great importance for classification. This paper presents a new nonparametric linear discriminant analysis method named Push–Pull marginal discriminant analysis (PPMDA), which takes full advantage of marginal information. For two-class cases, the idea of this method is to determine projected directions such that the marginal samples of one class are pushed away from the between-class marginal samples as far as possible and simultaneously pulled to the within-class samples as close as possible. This idea can be extended for multi-class cases and give rise to the PPMDA algorithm for feature extraction of multi-class problems. The proposed method is evaluated using the CENPARMI handwritten numeral database, the Extended Yale face database B and the ORL database. Experimental results show the effectiveness of the proposed method and its advantage after performance over the state-of-the-art feature extraction methods. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 31
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 53405244
- Full Text :
- https://doi.org/10.1016/j.patrec.2010.07.001