On Measure Transformed Canonical Correlation Analysis

TL;DR

This paper introduces measure transformed canonical correlation analysis (MTCCA), a novel method that applies transformations to joint probability measures to detect non-linear relationships more effectively than traditional LCCA and kernel CCA.

Contribution

The paper proposes a new framework, MTCCA, which transforms the joint probability distribution to enhance non-linear relationship detection with improved performance and lower complexity.

Findings

MTCCA effectively detects non-linear dependencies in simulated data.

MTCCA demonstrates advantages in measuring long-term associations in stock market data.

The approach offers performance benefits over kernel CCA.

Abstract

In this paper linear canonical correlation analysis (LCCA) is generalized by applying a structured transform to the joint probability distribution of the considered pair of random vectors, i.e., a transformation of the joint probability measure defined on their joint observation space. This framework, called measure transformed canonical correlation analysis (MTCCA), applies LCCA to the data after transformation of the joint probability measure. We show that judicious choice of the transform leads to a modified canonical correlation analysis, which, in contrast to LCCA, is capable of detecting non-linear relationships between the considered pair of random vectors. Unlike kernel canonical correlation analysis, where the transformation is applied to the random vectors, in MTCCA the transformation is applied to their joint probability distribution. This results in performance advantages and reduced implementation complexity. The proposed approach is illustrated for graphical model selection in simulated data having non-linear dependencies, and for measuring long-term associations between companies traded in the NASDAQ and NYSE stock markets.