Multivariate Maximal Correlation Analysis

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Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):775-783, 2014.

Abstract

Correlation analysis is one of the key elements of statistics, and has various applications in data analysis. Whereas most existing measures can only detect pairwise correlations between two dimensions, modern analysis aims at detecting correlations in multi-dimensional spaces. We propose MAC, a novel multivariate correlation measure designed for discovering multi-dimensional patterns. It belongs to the powerful class of maximal correlation analysis, for which we propose a generalization to multivariate domains. We highlight the limitations of current methods in this class, and address these with MAC. Our experiments show that MAC outperforms existing solutions, is robust to noise, and discovers interesting and useful patterns.

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