The Annals of Statistics

Tensor decompositions and sparse log-linear models

James E. Johndrow, Anirban Bhattacharya, and David B. Dunson

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Abstract

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Article information

Source
Ann. Statist. Volume 45, Number 1 (2017), 1-38.

Dates
Received: April 2014
Revised: November 2015
First available in Project Euclid: 21 February 2017

Permanent link to this document
https://projecteuclid.org/euclid.aos/1487667616

Digital Object Identifier
doi:10.1214/15-AOS1414

Zentralblatt MATH identifier
1367.62180

Subjects
Primary: 62F15: Bayesian inference

Keywords
Bayesian categorical data contingency table latent class analysis graphical model high-dimensional low rank Parafac Tucker sparsity

Citation

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B. Tensor decompositions and sparse log-linear models. Ann. Statist. 45 (2017), no. 1, 1--38. doi:10.1214/15-AOS1414. https://projecteuclid.org/euclid.aos/1487667616


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Supplemental materials

  • Supplement to: “Tensor decompositions and sparse log-linear models”. We provide a supplement with three parts. In the first part, we provide a proof of Remark 3.4 and a constructive proof of a bound on nonnegative rank for $d^{2}$ tensors corresponding to sparse log-linear models. The second part provides an MCMC algorithm for posterior computation in c-Tucker models and the third part provides supplementary figures and tables for Section 5.