First Advisor

Melanie Mitchell

Date of Publication

Summer 8-1-2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Computer Science

Department

Computer Science

Language

English

Subjects

Machine learning -- Mathematical models, Computer vision -- Mathematical models, Compressed sensing (Telecommunication)

DOI

10.15760/etd.1936

Physical Description

1 online resource (xv, 178 pages)

Abstract

Machine learning offers many powerful tools for prediction. One of these tools, the binary classifier, is often considered a black box. Although its predictions may be accurate, we might never know why the classifier made a particular prediction. In the first half of this dissertation, I review the state of the art of interpretable methods (methods for explaining why); after noting where the existing methods fall short, I propose a new method for a particular type of black box called additive networks. I offer a proof of trustworthiness for this new method (meaning a proof that my method does not "make up" the logic of the black box when generating an explanation), and verify that its explanations are sound empirically.

Sparse coding is part of a family of methods that are believed, by many researchers, to not be black boxes. In the second half of this dissertation, I review sparse coding and its application to the binary classifier. Despite the fact that the goal of sparse coding is to reconstruct data (an entirely different goal than classification), many researchers note that it improves classification accuracy. I investigate this phenomenon, challenging a common assumption in the literature. I show empirically that sparse reconstruction is not necessarily the right intermediate goal, when our ultimate goal is classification. Along the way, I introduce a new sparse coding algorithm that outperforms competing, state-of-the-art algorithms for a variety of important tasks.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

http://archives.pdx.edu/ds/psu/12491

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