Sparse least squares support vector machine with adaptive kernel parameters

Authors

Chaoyu YangFollow
Jie Yang, University of WollongongFollow
Jun Ma

RIS ID

142242

Publication Details

Yang, C., Yang, J. & Ma, J. (2020). Sparse least squares support vector machine with adaptive kernel parameters. International Journal of Computational Intelligence Systems, 13 (1), 212-222.

Abstract

© 2020 The Authors. In this paper, we propose an efficient Least Squares Support Vector Machine (LS-SVM) training algorithm, which incorporates sparse representation and dictionary learning. First, we formalize the LS-SVM training as a sparse representation process. Second, kernel parameters are adjusted by optimizing their average coherence. As such, the proposed algorithm addresses the training problem via generating the sparse solution and optimizing kernel parameters simultaneously. Experimental results demonstrate that the proposed algorithm is capable of achieving competitive performance compared to state-of-the-art approaches.