Licheng Jiao, Liefeng Bo and Ling Wang

Xidian University

Abstract: In this paper, we present two fast sparse approximation schemes for least squares support vector machine (LS-SVM) [2], named FSALS-SVM and PFSALS-SVM [1], to overcome the limitation of LS-SVM that it is not applicable to large data sets and to improve test speed. FSALS-SVM iteratively builds the decision function by adding one basis function from a kernel-based dictionary at one time. The process is terminated by using a flexible and stable epsilon insensitive stopping criterion. A probabilistic speedup scheme is employed to further improve the speed of FSALS-SVM and the resulting classifier is named PFSALS-SVM. Our algorithms are of two compelling features: low complexity and sparse solution. Experiments on benchmark data sets show that our algorithms obtain sparse classifiers at a rather low cost without sacrificing the generalization performance.

References

1. Licheng Jiao, Liefeng Bo, and Ling Wang, Fast Sparse Approximation for Least Square Support Vector Machine, IEEE Transactions on Neural Networks (TNN), vol. 18(3), pp. 685-697, 2007. [PDF] [BIB]

2. J. Suykens and J. Vandewalle, Least Squares Support Vector Machine Classifiers, Neural Process Letter, vol. 9, pp. 293-300, 1999.