Volume 13, Issue 1, 2020, Pages 212 - 222
Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters
Authors
Chaoyu Yang1, Jie Yang2, *, Jun Ma3
1School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, China
2School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia
3Operations Delivery Division, Sydney Trains, Alexandria, NSW, 2015, Australia
*Corresponding author. Email: jiey@uow.edu.au
Corresponding Author
Jie Yang
Received 19 November 2019, Accepted 28 January 2020, Available Online 2 March 2020.
- DOI
- 10.2991/ijcis.d.200205.001How to use a DOI?
- Keywords
- Least squares support vector machine; Sparse representation; Dictionary learning; Kernel parameter optimization
- Abstract
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.
- Copyright
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Chaoyu Yang AU - Jie Yang AU - Jun Ma PY - 2020 DA - 2020/03/02 TI - Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters JO - International Journal of Computational Intelligence Systems SP - 212 EP - 222 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200205.001 DO - 10.2991/ijcis.d.200205.001 ID - Yang2020 ER -