Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1273496.1273512 | ICML |
Keywords | Field | DocType |
feature selection,kernel space,data analysis,difficult problem,different state-of-the-art classification algorithm,selected feature,margin-based feature selection algorithm,informative feature,basis set,machine learning | Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Computer science,Kernel principal component analysis,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,String kernel,Variable kernel density estimation,Machine learning | Conference |
Citations | PageRank | References |
40 | 1.50 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Cao | 1 | 573 | 25.94 |
| Dou Shen | 2 | 1224 | 59.46 |
| Jian-Tao Sun | 3 | 1629 | 74.03 |
| Qiang Yang | 4 | 17039 | 875.69 |
| Zheng Chen | 5 | 5019 | 256.89 |