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Activity Number: 90
Type: Invited
Date/Time: Sunday, August 9, 2015 : 8:30 PM to 9:15 PM
Sponsor: Section on Nonparametric Statistics
Abstract #314730
Title: Convergence Analysis of Kernel Canonical Correlation Analysis
Author(s): Krishnakumar Balasubramanian* and Ming Yuan
Companies: University of Wisconsin - Madison and University of Wisconsin - Madison
Keywords: Kernel ; CCA ; Rates of convergence
Abstract:

Canonical correlation analysis (CCA) is a classical statistical technique to measure associations among two sets of random variables, with applications to several fields like multimodal signal processing and machine learning. In this talk, we first provide a direct and general formulation of Kernel CCA. We next present theoretical results for estimating the canonical correlation directions and the associated projection operators. Our results are based on certain concentration inequalities for the sample covariance and sample cross-covariance operators.

Authors who are presenting talks have a * after their name.

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