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Sparse least squares low rank kernel machines

Xu, D., Fang, M., Hong, X. ORCID: 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

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To link to this item DOI: 10.1007/978-3-030-36711-4_33


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
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:88397
Publisher:Springer, Cham

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