A General Framework for Association Analysis of Heterogeneous Data

TL;DR

This paper introduces a comprehensive framework for analyzing associations between high-dimensional, heterogeneous data types like continuous, binary, and count data, with applications in music annotation.

Contribution

It develops a novel modeling approach using exponential family distributions and structured decomposition to identify shared and unique patterns in heterogeneous data sets.

Findings

Effective analysis of CAL500 music data revealed meaningful acoustic-semantic relationships.

The framework enables automatic music annotation and retrieval.

The proposed methods outperform existing approaches in handling high-dimensional heterogeneous data.

Abstract

Multivariate association analysis is of primary interest in many applications. Despite the prevalence of high-dimensional and non-Gaussian data (such as count-valued or binary), most existing methods only apply to low-dimensional data with continuous measurements. Motivated by the Computer Audition Lab 500-song (CAL500) music annotation study, we develop a new framework for the association analysis of two sets of high-dimensional and heterogeneous (continuous/binary/count) data. We model heterogeneous random variables using exponential family distributions, and exploit a structured decomposition of the underlying natural parameter matrices to identify shared and individual patterns for two data sets. We also introduce a new measure of the strength of association, and a permutation-based procedure to test its significance. An alternating iteratively reweighted least squares algorithm is devised for model fitting, and several variants are developed to expedite computation and achieve variable selection. The application to the CAL500 data sheds light on the relationship between acoustic features and semantic annotations, and provides effective means for automatic music annotation and retrieval.