کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1149567 957887 2009 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Nonlinear measures of association with kernel canonical correlation analysis and applications
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
Nonlinear measures of association with kernel canonical correlation analysis and applications
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 139, Issue 7, 1 July 2009, Pages 2162–2174
نویسندگان
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