Abstract/Details

Combinatorial Markov Random Fields and their applications to information organization


2008 2008

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Abstract (summary)

We propose a new type of undirected graphical models called a Combinatorial Markov Random Field (Comraf) and discuss its advantages over existing graphical models. We develop an efficient inference methodology for Comrafs based on combinatorial optimization of information-theoretic objective functions; both global and local optimization schema are discussed. We apply Comrafs to multi-modal clustering tasks: standard (unsupervised) clustering, semi-supervised clustering, interactive clustering, and one-class clustering. For the one-class clustering task, we analytically show that the proposed optimization method is optimal under certain simplifying assumptions. We empirically demonstrate the power of Comraf models by comparing them to other state-of-the-art machine learning techniques, both in text clustering and image clustering domains. For unsupervised clustering, we show that Comrafs consistently and significantly outperform three previous state-of-the-art clustering techniques on six real-world textual datasets. For semi-supervised clustering, we show that the Comraf model is superior to a well-known constrained optimization method. For interactive clustering, Comraf obtains higher accuracy than a Support Vector Machine, trained on a large amount of labeled data. For one-class clustering, Comrafs demonstrate superior performance over two previously proposed methods. We summarize our thesis by giving a comprehensive recipe for machine learning modeling with Comrafs.

Indexing (details)


Subject
Artificial intelligence;
Computer science
Classification
0800: Artificial intelligence
0984: Computer science
Identifier / keyword
Applied sciences; Artificial intelligence; Machine learning; Multimodal learning; Unsupervised learning; Web mining
Title
Combinatorial Markov Random Fields and their applications to information organization
Author
Bekkerman, Ron
Number of pages
165
Publication year
2008
Degree date
2008
School code
0118
Source
DAI-B 69/07, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549663829
Advisor
Allan, James
Committee member
Cohen, Andrew; Croft, Bruce; Learned-Miller, Erik
University/institution
University of Massachusetts Amherst
Department
Computer Science
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3315504
ProQuest document ID
304565898
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304565898
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