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Title: Deep component analysis : algorithms and applications
Author: Trigeorgis, George
ISNI:       0000 0004 7229 4167
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Component Analysis (CA) methods have been crucial contributors for the large success of machine learning over the past decades. Although predominately all CA methods are linear models, depending on the formulation of the optimisation problem one can derive vastly different results tailored to different tasks. Such linear methods have the natural advantage of intepretability as they can easily be reasoned about, but also they are easy to fit onto the available data. Unfortunately, as they are mostly linear models they can not describe complex data distributions such as in-the-wild images of faces or, videos, or even auditory signals. On the other hand deep learning methodologies have excelled in modelling highly non-linear relations between highly heterogenious data distributions. Nonetheless, they were mostly used as black-boxes and usually required orders of magnitude more training samples than their linear counterparts to achieve similar performance. In this thesis we will aim to combine the best of both worlds. That is to incorporate the power of neural networks with the statistical intuition and the specially crafted ideas of component analysis methods. The result methodologies will have a diverse application set, solving problems from the areas of face clustering, timeseries alignment, domain adaptation, and face alignment in a mainly unsupervised way.
Supervisor: Zafeiriou, Stefanos ; Schuller, Bjoern Sponsor: Engineering and Physical Sciences Research Council ; Google
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral