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2015 IEEE International Conference on Data Mining Workshop (ICDMW) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
ISSN: 2375-9259
ISBN: 978-1-4673-8492-6
pp: 1163-1170
ABSTRACT
The proximal classifier with consistency (PCC) isan improvement of generalized eigenvalue proximal support vector machine (GEPSVM), ensuring consistency ignored inGEPSVM. However, similar to many other machine learning methods, PCC uses only the global information and the eigenvalue problem need to be solved, which can not classify small sample size (SSS) problem effectively. By exploiting both global andlocal information, we propose a novel binary classifier namedlocality sensitive proximal classifier with consistency (LSPCC). Our LSPCC determines two proximal hyperplanes by solving two small eigenvalue problems. This makes our LSPCC is ableto deal with SSS problem well. Experimental results on several standard small sample size datasets have shown the superiority of our approach.
INDEX TERMS
Eigenvalues and eigenfunctions, Support vector machines, Training, Electronic mail, Manifolds, Kernel, Conferences,small sample size problem, pattern recognition, proximal classifier, local learning, eigenvalue decomposition
CITATION
Yuan-Hai Shao, Zhen Wang, Chun-Na Li, Nai-Yang Deng, "Locality Sensitive Proximal Classifier with Consistency for Small Sample Size Problem", 2015 IEEE International Conference on Data Mining Workshop (ICDMW), vol. 00, no. , pp. 1163-1170, 2015, doi:10.1109/ICDMW.2015.180
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