Document Detail


Multiple view clustering using a weighted combination of exemplar-based mixture models.
MedLine Citation:
PMID:  20934949     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis.
Authors:
Grigorios F Tzortzis; Aristidis C Likas
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Publication Detail:
Type:  Journal Article     Date:  2010-10-07
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  21     ISSN:  1941-0093     ISO Abbreviation:  IEEE Trans Neural Netw     Publication Date:  2010 Dec 
Date Detail:
Created Date:  2010-12-07     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1925-38     Citation Subset:  IM    
Affiliation:
Department of Computer Science, University of Ioannina, Greece. gtzortzi@cs.uoi.gr
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