Learning High-dimensional Latent Variable Models via Doubly Stochastic Optimisation by Unadjusted Langevin

Abstract

Latent variable models are widely used in social and behavioural sciences, such as education, psychology, and political science. In recent years, high-dimensional latent variable models have become increasingly common for analysing large and complex data. Estimating high-dimensional latent variable models using marginal maximum likelihood is computationally demanding due to the complexity of integrals involved. To address this challenge, stochastic optimisation, which combines stochastic approximation and sampling techniques, has been shown to be effective. This method iterates between two steps -- (1) sampling the latent variables from their posterior distribution based on the current parameter estimate, and (2) updating the fixed parameters using an approximate stochastic gradient constructed from the latent variable samples. In this paper, we propose a computationally more efficient stochastic optimisation algorithm. This improvement is achieved through the use of a minibatch of observations when sampling latent variables and constructing stochastic gradients, and an unadjusted Langevin sampler that utilises the gradient of the negative complete-data log-likelihood to sample latent variables. Theoretical results are established for the proposed algorithm, showing that the iterative parameter update converges to the marginal maximum likelihood estimate as the number of iterations goes to infinity. Furthermore, the proposed algorithm is shown to scale well to high-dimensional settings through simulation studies and a personality test application with 30,000 respondents, 300 items, and 30 latent dimensions.