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

1.1. OVERVIEW OF THE THESIS 11
reduction the aim is to find the data’s true or intrinsic parameterization from the
capturing device representation.
Many tasks in computer science are associated with data coming from multiple
streams or views of the same underlying phenomenon. Often each view provide
complementary information about the data. For modeling purposes it is there-
fore of interest to use information from each view. The task of merging several
different views are called Feature Fusion.
The work undertaken in this thesis spans both realms presented above. Given
multiple views of the same phenomenon we create models which are capable of
leveraging the advantage of each view in learning a reduced dimensional repre-
sentation of the data.
1.1 Overview of the thesis
A brief outline of the dissertation follows,
Chapter 2 This chapter provides the motivation and the background to the
machine learning task of dimensionality reduction. The two different approaches
to dimensionality reduction, spectral and generative, are introduced and their
strengths and weaknesses reviewed. We continue by introducing Gaussian Pro-
cesses (GP) and give a brief background to Bayesian Modeling. The Gaussian
Process Latent Variable Model (GP-LVM) [33, 32] a dimensionality reduction
model based on Gaussian Processes is introduced. Further, we introduce the task
of Shared Dimensionality Reduction which will be the main focus of this thesis.
Chapter 3 This chapter describes the two shared generative dimensionality
reduction models developed in this thesis. By motivating the short-comings of