Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite Graphical Models

TL;DR

This paper introduces a tensor unfolding method to prove a new, constructive identifiability result for discrete bipartite graphical models, enabling structure learning even with nonlinear dependencies.

Contribution

The paper presents a novel tensor unfolding technique that provides a constructive proof of graph identifiability for discrete bipartite models, including nonlinear dependencies.

Findings

Provides a population-level structure learning algorithm.

Allows for nonlinear dependence among variables.

Identifiability condition is interpretable and practical.

Abstract

We use a tensor unfolding technique to prove a new identifiability result for discrete bipartite graphical models, which have a bipartite graph between an observed and a latent layer. This model family includes popular models such as Noisy-Or Bayesian networks for medical diagnosis and Restricted Boltzmann Machines in machine learning. These models are also building blocks for deep generative models. Our result on identifying the graph structure enjoys the following nice properties. First, our identifiability proof is constructive, in which we innovatively unfold the population tensor under the model into matrices and inspect the rank properties of the resulting matrices to uncover the graph. This proof itself gives a population-level structure learning algorithm that outputs both the number of latent variables and the bipartite graph. Second, we allow various forms of nonlinear dependence among the variables, unlike many continuous latent variable graphical models that rely on linearity to show identifiability. Third, our identifiability condition is interpretable, only requiring each latent variable to connect to at least two "pure" observed variables in the bipartite graph. The new result not only brings novel advances in algebraic statistics, but also has useful implications for these models' trustworthy applications in scientific disciplines and interpretable machine learning.