Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14945
Title: Generalized locality preserving Maxi-Min Margin Machine
Authors: Zhang, Z
Choi, KS 
Luo, X
Wang, S
Keywords: Large margin classification
Locality preserving projections classification
Maxi-Min Margin Machine
Issue Date: 2012
Publisher: Pergamon-Elsevier Science Ltd
Source: Neural networks, 2012, v. 36, p. 18-24 How to cite?
Journal: Neural Networks 
Abstract: Research on large margin classifiers from the "local" and "global" view has become an active topic in machine learning and pattern recognition. Inspired from the typical local and global learning machine Maxi-Min Margin Machine (M4) and the idea of the Locality Preserving Projections (LPP), we propose a novel large margin classifier, the Generalized Locality Preserving Maxi-Min Margin Machine (GLPM), where the within-class matrices are constructed using the labeled training points in a supervised way, and then used to build the classifier. The within-class matrices of GLPM preserve the intra-class manifold in the training sets, as well as the covariance matrices which indicate the global projection direction in the M4 model. Moreover, the connections among GLPM, M4 and LFDA are theoretically analyzed, and we show that GLPM can be considered as a generalized M4 machine. The GLPM is also more robust since it requires no assumption on data distribution while Gaussian data distribution is assumed in the M4 machine. Experiments on data sets from the machine learning repository demonstrate its advantage over M4 in both local and global learning performance.
URI: http://hdl.handle.net/10397/14945
DOI: 10.1016/j.neunet.2012.08.007
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

2
Last Week
0
Last month
0
Citations as of Aug 20, 2017

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
0
Citations as of Aug 20, 2017

Page view(s)

33
Last Week
2
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



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