Sparse Least Squares Low Rank Kernel Machines
Machine Learning
2019-10-22 v2 Artificial Intelligence
Machine Learning
Abstract
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.
Cite
@article{arxiv.1901.10098,
title = {Sparse Least Squares Low Rank Kernel Machines},
author = {Di Xu and Manjing Fang and Xia Hong and Junbin Gao},
journal= {arXiv preprint arXiv:1901.10098},
year = {2019}
}
Comments
2019 ICONIP