Sciweavers

Share
ICML
2007
IEEE

Feature selection in a kernel space

9 years 12 months ago
Feature selection in a kernel space
We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult problem because the dimension of a kernel space may be infinite. In the past, little work has been done on feature selection in a kernel space. To solve this problem, we derive a basis set in the kernel space as a first step for feature selection. Using the basis set, we then extend the margin-based feature selection algorithms that are proven effective even when many features are dependent. The selected features form a subspace of the kernel space, in which different state-of-the-art classification algorithms can be applied for classification. We conduct extensive experiments over real and simulated data to compare our proposed method with four baseline algorithms. Both theoretical analysis and experimental results validate the effectiveness of our proposed method. Appearing in Proceedings of the 24th Intern...
Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng Chen
Comments (0)
books