| 题名: | Nonlinear measures of association with kernel canonical correlation analysis and applications |
| 作者: | Su-Yun Huang
Mei-Hsien Lee
Chuhsing Kate Hsiao
李美賢 |
| 贡献者: | 臺北市立教育大學數學資訊教育學系 |
| 关键词: | Association measure
Canonical correlation analysis
Dimension reduction
Kernel method
Multivariate analysis
Reproducing kernel
Reproducing kernel Hilbert space
Test of independence |
| 日期: | 2009 |
| 上传时间: | 2009-08-04 10:47:49 (UTC+8) |
| 摘要: | Measures of association between two sets of random variables have long been of interest to statisticians. The classical canonical correlation analysis (LCCA) can characterize, but also is limited to, linear association. This article introduces a nonlinear and nonparametric kernel method for association study and proposes a new independence test for two sets of variables. This nonlinear kernel canonical correlation analysis (KCCA) can also be applied to the nonlinear discriminant analysis. Implementation issues are discussed. We place the implementation of KCCA in the framework of classical LCCA via a sequence of independent systems in the kernel associated Hilbert spaces. Such a placement provides an easy way to carry out the KCCA. Numerical experiments and comparison with other nonparametric methods are presented. |
| 關聯: | Journal of Statistical Planning and Inference, V139(7), P.2162-2174 |
| 显示于类别: | [數學系(含數學教育碩士班)] 期刊論文
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