Shared Gaussian Process Latent Variables Models - Oxford Brookes ...

Abstract
A fundamental task is machine learning is modeling the relationship between dif-
ferent observation spaces. Dimensionality reduction is the task reducing the num-
ber of dimensions in a parameterization of a data-set. In this thesis we are inter-
ested in the cross-road between these two tasks: shared dimensionality reduction.
Shared dimensionality reduction aims to represent multiple observation spaces
within the same model. Previously suggested models have been limited to the
scenarios where the observations have been generated from the same manifold.
In this paper we present a Gaussian process Latent Variable Model (GP-LVM)
[33] for shared dimensionality reduction without making assumptions about the
relationship between the observations. Further we suggest an extension to Canon-
ical Correlation Analysis (CCA) called Non Consolidating Component Analy-
sis (NCCA). The proposed algorithm extends classical CCA to represent the full
variance of the data opposed to only the correlated. We compare the suggested
GP-LVM model to existing models and show results on real-world problems ex-
emplifying the advantages of our approach.