The Annals of Applied Statistics

A general framework for association analysis of heterogeneous data

Gen Li and Irina Gaynanova

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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.

Article information

Source
Ann. Appl. Stat., Volume 12, Number 3 (2018), 1700-1726.

Dates
Received: February 2017
Revised: November 2017
First available in Project Euclid: 11 September 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1536652971

Digital Object Identifier
doi:10.1214/17-AOAS1127

Mathematical Reviews number (MathSciNet)
MR3852694

Keywords
Exponential family inter-battery factor analysis joint and individual structure matrix decomposition generalized linear model association coefficient

Citation

Li, Gen; Gaynanova, Irina. A general framework for association analysis of heterogeneous data. Ann. Appl. Stat. 12 (2018), no. 3, 1700--1726. doi:10.1214/17-AOAS1127. https://projecteuclid.org/euclid.aoas/1536652971


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Supplemental materials

  • Supplementary Material for A General Framework for Association Analysis of Heterogeneous Data. We provide proofs, technical details of the algorithm, a detailed description of the rank estimation procedure, and additional simulation results in the supplementary material.