Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

TL;DR

This paper improves Gaussian Process Latent Variable Models (GPLVM) by introducing a new training strategy that enhances their density estimation capabilities and generalization performance on complex, high-dimensional data.

Contribution

The authors propose a novel training method for GPLVMs, enabling them to serve as effective density models for high-dimensional data.

Findings

Enhanced density estimation in benchmark datasets

Improved generalization performance of GPLVMs

Better modeling of low-dimensional manifolds in high-dimensional data

Abstract

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.