Clustering model selection for reduced support vector machines

Lih Ren Jen*, Yuh-Jye Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The reduced support vector machine was proposed for the practical objective that overcomes the computational difficulties as well as reduces the model complexity by generating a nonlinear separating surface for a massive dataset. It has been successfully applied to other kernel-based learning algorithms. Also, there are experimental studies on RSVM that showed the efficiency of RSVM. In this paper we propose a robust method to build the model of RSVM via RBF (Gaussian) kernel construction. Applying clustering algorithm to each class, we can generate cluster centroids of each class and use them to form the reduced set which is used in RSVM. We also estimate the approximate density for each cluster to get the parameter used in Gaussian kernel. Under the compatible classification performance on the test set, our method selects a smaller reduced set than the one via random selection scheme. Moreover, it determines the kernel parameter automatically and individually for each point in the reduced set while the RSVM used a common kernel parameter which is determined by a tuning procedure.

Keywords

  • Gaussian Kernel
  • k-means Clustering Algorithm
  • Model Selection
  • Radial Basis Function Network
  • Reduced Set
  • Support Vector Machine

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