Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26429
Title: Matrix pattern based minimum within-class scatter support vector machines
Authors: Gao, J
Chung, FL 
Wang, S
Keywords: Face recognition
Matrix pattern
SVM
Within-class scatter matrix
Issue Date: 2011
Publisher: Elsevier
Source: Applied soft computing, 2011, v. 11, no. 8, p. 5602-5610 How to cite?
Journal: Applied soft computing 
Abstract: Based on minimum within-class scatter support vector machines (MCSVM), a new matrix pattern based MCSSVM (MCSVM matrix) is presented. Accordingly, it is extended by introducing Mercer's kernels in order to solve the problem of nonlinear decision boundaries, which presents a significant matrix pattern based nonlinear support vector machines: Ker-MCSVM matrix. The above-mentioned approaches not only keep the merits of MCSVM, but, owing to introducing matrix pattern based within-class scatter matrix into support vector machines, theoretically better solve the singular problem of within-class scatter matrix when small sample size problems are dealt with, reduce the time/place complexity when within-class scatter matrix, its invertible matrix and weight vector ?s are calculated. Hence, the classification accuracy is improved to certain extent. Experimental results indicate the above advantages of the proposed methods: both MCSVM matrix and Ker-MCSVM matrix.
URI: http://hdl.handle.net/10397/26429
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2011.04.004
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

5
Last Week
1
Last month
0
Citations as of Aug 13, 2017

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Aug 4, 2017

Page view(s)

28
Last Week
1
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



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