Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9420
Title: Push-Pull marginal discriminant analysis for feature extraction
Authors: Gu, Z
Yang, J
Zhang, L 
Keywords: Classification
Feature extraction
Linear discriminant analysis
Nonparametric methods
Issue Date: 2010
Publisher: North-Holland
Source: Pattern recognition letters, 2010, v. 31, no. 15, p. 2345-2352 How to cite?
Journal: Pattern recognition letters 
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.
URI: http://hdl.handle.net/10397/9420
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/j.patrec.2010.07.001
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

6
Last Week
0
Last month
1
Citations as of Dec 4, 2018

WEB OF SCIENCETM
Citations

6
Last Week
0
Last month
0
Citations as of Sep 23, 2018

Page view(s)

74
Last Week
1
Last month
Citations as of Dec 9, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.