کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
534927 | 870306 | 2010 | 8 صفحه PDF | دانلود رایگان |
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.
Journal: Pattern Recognition Letters - Volume 31, Issue 15, 1 November 2010, Pages 2345–2352