Enriched Mixtures of Gaussian Process Experts
This addresses scalability and performance issues in probabilistic modeling for high-dimensional data, such as in medical applications, though it is an incremental improvement over existing mixture methods.
The paper tackled the poor scalability and excessive expert count in mixtures of Gaussian process experts for multi-dimensional inputs, resulting in a novel nested partitioning model that effectively estimated parsimonious probabilistic descriptions on synthetic and Alzheimer's data.
Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally. Combined with Gaussian process (GP) experts, this results in a powerful and highly flexible model. We focus on alternative mixtures of GP experts, which model the joint distribution of the inputs and targets explicitly. We highlight issues of this approach in multi-dimensional input spaces, namely, poor scalability and the need for an unnecessarily large number of experts, degrading the predictive performance and increasing uncertainty. We construct a novel model to address these issues through a nested partitioning scheme that automatically infers the number of components at both levels. Multiple response types are accommodated through a generalised GP framework, while multiple input types are included through a factorised exponential family structure. We show the effectiveness of our approach in estimating a parsimonious probabilistic description of both synthetic data of increasing dimension and an Alzheimer's challenge dataset.