Nonlinear measures of association with kernel canonical correlation analysis and applications

University of Taipei > 理學院 > 數學系(含數學教育碩士班) > 期刊論文 > Item 987654321/15285

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://utaipeir.lib.utaipei.edu.tw/dspace/handle/987654321/15285

题名:	Nonlinear measures of association with kernel canonical correlation analysis and applications
作者:	Huang, S. Y.;Lee, M. H.;李美賢;Hsiao, C. K.
日期:	2008-10
上传时间:	2016-04-28 15:29:51 (UTC+8)
關聯:	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. Nu-merical experiments and comparison with other nonparametric methods are presented. Nonlinear measures of association with kernel canonical correlation analysis and applications. Available from: https://www.researchgate.net/publication/228570577_Nonlinear_measures_of_association_with_kernel_canonical_correlation_analysis_and_applications [accessed Apr 14, 2016].
显示于类别:	[數學系(含數學教育碩士班)] 期刊論文

文件中的档案:

没有与此文件相关的档案.

在uTaipei中所有的数据项都受到原著作权保护.