We study how to perform effective Bayesian inference in high-dimensional sparse Factor
Analysis models with a zero-norm, sparsity-inducing prior on the model parameters. Such …

There has been increased research interest in the subfield of sparse Bayesian factor
analysis with shrinkage priors, which achieve additional sparsity beyond the natural …

Dimension reduction algorithms aim to discover latent variables which describe underlying
structures in high-dimensional data. Traditional methods such as principal component …

In this paper, we propose a new Bayesian inference method for a highdimensional sparse
factor model that allows both the factor dimensionality and the sparse structure of the …

Factor analysis is a general purpose technique for dimensionality reduction with
applications in diverse areas including computer vision, collaborative filtering and …

Sparse factor analysis comprises aspects of exploratory and confirmatory factor analysis,
seeking to establish a parsimonious structure in the loadings matrix of the model. This task is …

We focus on sparse modelling of high-dimensional covariance matrices using Bayesian
latent factor models. We propose a multiplicative gamma process shrinkage prior on the …

Factor models aim to describe a dependence structure among high-dimensional random
variables in terms of a low-dimensional unobserved random vector called a factor. One of …

SUMMARY I discuss Bayesian factor regression models with many explanatory variables.
These models are of particular interest and applicability in problems of prediction, but also …

Factor analysis is used to explore the covariance structure among a set of observed random
variables. The technique constructs a reduced number of random variables called common …