Convergence analysis of kernel Canonical Correlation Analysis: theory and practice
Convergence analysis of kernel Canonical Correlation Analysis: theory and practice
复制标题
DOI：
10.1007/s10994-008-5085-3
复制
发表时间：
2009-01-01
期刊：
影响因子：
7.5
通讯作者：
Shawe-Taylor, John
中科院分区：
文献类型：
--
作者：
Hardoon, David R.;Shawe-Taylor, John
规范相关性分析是一种用于查找一对矢量的技术，可最大化一组配对变量的相关性，这些对可以视为同一对象的两个视图。本文通过定义捕获两个视图的特征相似的程度的模式函数来提供对规范相关分析的收敛分析。我们使用Rademacher的复杂性分析了收敛性，因此得出了针对新数据绑定的误差。该分析为内核规范相关分析的正则化提供了进一步的理由，并通过对现实世界数据的实验来证实。
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 Correlation 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 experiments on real world data.