[Studies of spectra classification based on kernel covering algorithm].
A kernel based covering algorithm, called the kernel covering algorithm (KCA), is proposed for the classification of celestial spectra. This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space. The experiments show that the classification result based on KCA is a little less than that based on SVM. However, KCA only involves the distance computation without the need to solve the quadratic programming problem. Also, KCA is insensitive to the width of gauss window. Although KCA has a comparable classification performance with the covering algorithm, it changes the distance between samples in feature space by the nonlinear mapping such that the distribution of samples is more adaptable to classify. Therefore, the number of KCA's resulting support vectors is significantly smaller than that of the covering algorithm.
Yang
Jin-fu
JF
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. yangjf@nlpr.ia.ac.cn
Xu
Xin
X
Wu
Fu-chao
FC
Zhao
Yong-heng
YH