GP-select: Accelerating EM using adaptive subspace preselection
This addresses computational bottlenecks in probabilistic inference for researchers and practitioners, though it is incremental as it builds on existing EM and selection function methods.
The authors tackled the problem of fast inference in generative graphical models with many latent states by proposing GP-select, a nonparametric procedure that uses adaptive subspace preselection to accelerate EM. They achieved results matching a customized state-of-the-art selection method for object localization with occlusion at a far lower computational cost.
We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a 'selection function' to reveal the relevant latent variables, and use this to obtain a compact approximation of the posterior distribution for EM; this can make inference possible where the number of possible latent states is e.g. exponential in the number of latent variables, whereas an exact approach would be computationally unfeasible. We learn the selection function entirely from the observed data and current EM state via Gaussian process regression. This is by contrast with earlier approaches, where selection functions were manually-designed for each problem setting. We show that our approach performs as well as these bespoke selection functions on a wide variety of inference problems: in particular, for the challenging case of a hierarchical model for object localization with occlusion, we achieve results that match a customized state-of-the-art selection method, at a far lower computational cost.