PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

We propose a new multi-view clustering method which uses clustering results obtained on each view as a voting pattern in order to construct a new set of multi-view clusters. Our experiments on a multilingual corpus of documents show that performance increases significantly over simple concatenation and another multi-view clustering technique.
Young-Min Kim, Massih Amini, Cyril Goutte and Patrick Gallinari
In: SIGIR 2010, 19-23 July 2010, Geneva, Switzerland.

Abstract

We propose a new multi-view clustering method which uses clustering results obtained on each view as a voting pattern in order to construct a new set of multi-view clusters. Our experiments on a multilingual corpus of documents show that performance increases significantly over simple concatenation and another multi-view clustering technique.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:9298
Deposited By:Massih Amini
Deposited On:22 February 2012