PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Convergence Analysis of Kernel Canonical Correlation Analysis: Theory and Practice
David Hardoon and John Shawe-Taylor
Machine Learning Journal Volume 74, Number 1, pp. 23-38, 2009.

Abstract

Canonical Correlation Analysis is a technique for finding pairs of basis vectors that maximise the correlation of a set of paired variables, these pairs can be considered as two views of the same object. This paper provides a convergence analysis of Canonical Cor- relation Analysis by defining a pattern function that captures the degree to which the features from the two views are similar. We analyse the convergence using Rademacher complexity, hence deriving the error bound for new data. The analysis provides further justification for the regularisation of kernel Canonical Correlation Analysis and is corroborated by experi- ments on real world data.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5879
Deposited By:David Hardoon
Deposited On:08 March 2010