Sparse kernel minimum squared error using Householder transformation and givens rotation

2017 ◽  
Vol 48 (2) ◽  
pp. 390-415
Author(s):  
Yong-Ping Zhao ◽  
Peng-Peng Xi ◽  
Bing Li ◽  
Zhi-Qiang Li
2016 ◽  
Vol 171 ◽  
pp. 149-155 ◽  
Author(s):  
Haitao Gan ◽  
Rui Huang ◽  
Zhizeng Luo ◽  
Yingle Fan ◽  
Farong Gao

2014 ◽  
Vol 536-537 ◽  
pp. 450-453 ◽  
Author(s):  
Jiang Jiang ◽  
Xi Chen ◽  
Hai Tao Gan

In this paper, a sparsity based model is proposed for feature selection in kernel minimum squared error (KMSE). By imposing a sparsity shrinkage term, we formulate the procedure of subset selection as an optimization problem. With the chosen small portion of training examples, the computational burden of feature extraction is largely alleviated. Experimental results conducted on several benchmark datasets indicate the effectivity and efficiency of our method.


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