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.

Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/88397
Item Type	Book or Report Section
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer, Cham
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