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On the Sparseness and Generalization Capability of Least Squares Support Vector Machines

Author

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  • Yan Aijun

    (College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing100124, China)

  • Huang Xiaoqian

    (College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing100124, China)

  • Shao Hongshan

    (College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing100124, China)

Abstract

Compared with standard support vector machines (SVM), sparseness is lost in the modeling process of least squares support vector machines (LS-SVM), causing limited generalization capability. An improved method using quadratic renyi-entropy pruning is presented to deal with the above problems. First, a kernel principal component analysis (KPCA) is used to denoise the training data. Next, the authors use the genetic algorithm to estimate and optimize the kernel function parameter and penalty factor. Then, pick the subset that has the largest quadratic entropy to train and prune, and repeat this process until the cumulative error rate reaches the condition requirement. Finally, comparing experiments on the data classification and regression indicates that the proposed method is effective and may improve the sparseness and the generalization capability of LS-SVM model.

Suggested Citation

  • Yan Aijun & Huang Xiaoqian & Shao Hongshan, 2015. "On the Sparseness and Generalization Capability of Least Squares Support Vector Machines," Journal of Systems Science and Information, De Gruyter, vol. 3(3), pages 279-288, June.
    • Handle: RePEc:bpj:jossai:v:3:y:2015:i:3:p:279-288:n:6
    DOI: 10.1515/JSSI-2015-0279
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    References listed on IDEAS

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    1. X. Hong & S. Chen & C.J. Harris, 2012. "Using zero-norm constraint for sparse probability density function estimation," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(11), pages 2107-2113.
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