UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Statistical consistency of kernel canonical correlation analysis

Fukumizu, K; Bach, FR; Gretton, A; (2007) Statistical consistency of kernel canonical correlation analysis. Journal of Machine Learning Research , 8 pp. 361-383.

Full text not available from this repository.

Abstract

While kernel canonical correlation analysis (CCA) has been applied in many contexts, the convergence of finite sample estimates of the associated functions to their population counterparts has not yet been established. This paper gives a mathematical proof of the statistical convergence of kernel CCA, providing a theoretical justification for the method. The proof uses covariance operators defined on reproducing kernel Hilbert spaces, and analyzes the convergence of their empirical estimates of finite rank to their population counterparts, which can have infinite rank. The result also gives a sufficient condition for convergence on the regularization coefficient involved in kernel CCA: this should decrease as n -1/3 , where n is the number of data.

Type: Article
Title: Statistical consistency of kernel canonical correlation analysis
URI: http://discovery.ucl.ac.uk/id/eprint/1334334
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item