Stability Analysis of Kernel Canonical Correlation Analysis: Theory and Practice
David Hardoon and John Shawe-Taylor
Machine Learning 2007.

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 stability analysis of Canonical Correlation Analysis by defining a pattern function that captures the degree to which the features from the two views are similar. We analyse the stability using Rademacher complexity, hence deriving the error bound for new data. The analysis justifies the regularisation of kernel Canonical Correlation Analysis and is corroborated in experiments on real world data.

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