On the identifiability of Bayesian factor analytic models

Abstract

A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. We introduce a post-processing scheme in order to deal with rotation, sign and permutation invariance of the MCMC sample. The exact version of the contributed algorithm requires to solve $2^q$ assignment problems per (retained) MCMC iteration, where $q$ denotes the number of factors of the fitted model. For large numbers of factors two approximate schemes based on simulated annealing are also discussed. We demonstrate that the proposed method leads to interpretable posterior distributions using synthetic and publicly available data from typical factor analytic models as well as mixtures of factor analyzers. An R package is available online at CRAN web-page.

to appear in STCO

References in corpus (1)

• Model-based clustering based on sparse finite Gaussian mixtures

Cited by in corpus (4)

• Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings
• Graph link prediction in computer networks using Poisson matrix factorisation
• Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching
• Dynamic Factor Analysis with Dependent Gaussian Processes for High-Dimensional Gene Expression Trajectories