Sparse least squares low rank kernel machines

Xu, D., Fang, M., Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Gao, J. (2019) Sparse least squares low rank kernel machines. In: International Conference on Neural Information Processing. Springer, Cham, pp. 395-406. ISBN 9783030367107 doi: 10.1007/978-3-030-36711-4_33

Abstract/Summary

A general framework of least squares support vector machine with low rank kernels, referred to as LR-LSSVM, is introduced in this paper. The special structure of low rank kernels with a controlled model size brings sparsity as well as computational efficiency to the proposed model. Meanwhile,a two-step optimization algorithm with three different criteria is proposed and various experiments are carried out using the example of the so-call robust RBF kernel to validate the model. The experiment results show that the performance of the proposed algorithm is comparable or superior to several existing kernel machines.