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Regularized factor models Open Access


Other title
machine learning
artificial intelligence
unsupervised learning
Type of item
Degree grantor
University of Alberta
Author or creator
White, Martha
Supervisor and department
Schuurmans, Dale (Computing Science)
Bowling, Michael (Computing Science)
Examining committee member and department
Greiner, Russ (Computing Science)
Gyorgy, Andras (Computing Science)
Gordon, Geoffrey (Machine Learning)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
This dissertation explores regularized factor models as a simple unification of machine learn- ing problems, with a focus on algorithmic development within this known formalism. The main contributions are (1) the development of generic, efficient algorithms for a subclass of regularized factorizations and (2) new unifications that facilitate application of these algorithms to problems previously without known tractable algorithms. Concurrently, the generality of the formalism is further demonstrated with a thorough summary of known, but often scattered, connections between supervised and unsupervised learning problems and algorithms. The dissertation first presents the main algorithmic advances: convex reformulations of non- convex regularized factorization objectives. A convex reformulation is developed for a general subset of regularized factor models, with an efficiently computable optimization for five different regularization choices. The thesis then describes advances using these generic convex reformulation techniques in three important problems: multi-view subspace learning, semi-supervised learn- ing and estimating autoregressive moving average models. These novel settings are unified under regularized factor models by incorporating problem properties in terms of regularization. Once ex- pressed as regularized factor models, we can take advantage of the convex reformulation techniques to obtain novel algorithms that produce global solutions. These advances include the first global estimation procedure for two-view subspace learning and for autoregressive moving average models. The simple algorithms obtained from these general convex reformulation techniques are empirically shown to be effective across these three problems on a variety of datasets. This dissertation illustrates that many problems can be specified as a simple regularized factorization, that this class is amenable to global optimization and that it is advantageous to represent machine learning problems as regularized factor models.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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