A review of bayesian methods for infinite factorisations
File(s)
Preprint
OA Location
Author(s)
Grushanina, Margarita
Type
preprint
Abstract
Defining the number of latent factors has been one of the most challenging problems in factor analysis. Infinite factor models offer a solution to this problem by applying increasing shrinkage on the columns of factor loading matrices, thus penalising increasing factor dimensionality. The adaptive MCMC algorithms used for inference in such models allow to defer the dimension of the latent factor space automatically based on the data. This paper presents an overview of Bayesian models for infinite factorisations with some discussion on the properties of such models as well as their comparative advantages and drawbacks.
Date Issued
2023-09-22
Citation
arXiv, 2023
Journal / Book Title
arXiv
Copyright Statement
Copyright © 2023 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://arxiv.org/abs/2309.12990
Subjects
Factor analysis
adaptive Gibbs sampling
spike-and-slab prior
Indian buffet process
multiplicative gamma process
increasing shrinkage