Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

Abstract

We proposed a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We show how the c-GPLVM can extract low-dimensional structures from highdimensional data sets whilst allowing a breakdown of feature-level variability that is not present in other commonly used dimensionality reduction approaches.

Publication
In: International Conference on Machine Learning, pp. 4372–4381