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Abstract: While kernel canonical correlation analysis (kernel CCA) has been applied in many problems, the asymptotic convergence of the functions estimated from a finite sample to the true functions has not yet been established. This paper gives a rigorous proof of the statistical convergence of kernel CCA and a related method (NOCCO), which provides a theoretical justification for these methods. The result also gives a su#cient condition on the decay of the regularization coe#cient in the methods ... (Update)
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BibTeX entry: (Update)
@misc{ institute-statistical,
author = "Kenji Fukumizu Institute",
title = "Statistical Convergence of Kernel CCA",
url = "citeseer.ist.psu.edu/757835.html" }
Citations (may not include all citations):114 Linear Operators (context) - Dunford, Schwartz - 1963
51 Kernel independent component analysis - Bach, Jordan - 2002
14 American Mathematical Society (context) - Aronszajn, reproducing et al. - 1950
13 Canonical correlation analysis when the data are curves (context) - Leurgans, Moyeed et al. - 1993
4 Joint measures and cross-covariance operators (context) - Baker - 1973
4 Dimensionality reduction for supervised learning with reprod.. (context) - Fukumizu, Bach et al. - 2004
3 A kernel method for canonical correlation analysis - Akaho
2 Measuring statistical dependence with Hilbert-Schmidt norms (context) - Gretton, Bousquet et al. - 2005
2 Nonlinear feature extraction using generalized canonical cor.. (context) - Melzer, Reiter et al.
1 MaxPlanck -Institut fur biologische Kybernetik (context) - Gretton, Smola et al. - 2004
1 Consistency of kernel canonical correlation (context) - Fukumizu, Bach et al. - 2005
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