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AIC Seminar Series

Multi-Modal Clustering: a Formal Framework and Practical Applications

Ron BekkermanUniversity of Massachusetts Amherst

Notice:  hosted by Jeffrey Davitz

Date:  Monday, August 6th 2007 at 10:00am

Location:  EJ228 (SRI E building)  (Directions)


In this talk, after giving a brief overview of my research, I will focus on one of my favorite topics in machine learning: multi-modal clustering, which is a clustering problem in the environment where multiple views (or modalities) of the input data are available. In text clustering, for instance, modalities are documents, their words, their authors, titles, markup primitives etc. Multi-modal clustering is a problem of simultaneously constructing N partitionings of N data modalities, which reduces statistical sparseness of data representation, and potentially leads to more accurate clusterings than those obtained separately.

First, I will present Combinatorial Markov Random Fields (Comrafs) that I have recently proposed as a formal framework for multi-modal clustering. Comrafs have proved themselves to be the current state-of-the-art in document clustering. Second, I will discuss particular modeling choices that result in deriving a variety of Comraf models not only for basic text clustering, but also for image clustering, semi-supervised and interactive clustering, and other tasks. Finally, I will address the efficiency issue and propose a Comraf model able to accurately cluster a multi-million document collection.

   Bio for Ron Bekkerman

Ron Bekkerman is a PhD candidate at the University of Massachusetts Amherst, working under the supervision of Prof. James Allan on unsupervised and semi-supervised learning problems, with applications to information retrieval and Web mining. Ron received his BSc and MSc in CS from the Technion—Israel Institute of Technology. His Masters thesis was on feature induction for text categorization.

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