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The Scientific World Journal
Volume 2014, Article ID 872697, 6 pages
Research Article

A Novel Support Vector Machine with Globality-Locality Preserving

Institute of Metrology and Computational Science, China Jiliang University, Hangzhou, Zhejiang 310018, China

Received 25 April 2014; Accepted 27 May 2014; Published 17 June 2014

Academic Editor: Shan Zhao

Copyright © 2014 Cheng-Long Ma and Yu-Bo Yuan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.