The paper introduces a framework for clustering data objects in a similarity-based context. The aim is to cluster objects into a given number of classes without imposing a hard partition, but allowing for a soft assignment of objects to clusters. Our approach uses the assumption that similarities reflect the likelihood of the objects to be in a same class in order to derive a probabilistic model for estimating the unknown cluster assignments. This leads to a polynomial optimization in probability domain, which is tackled by means of a result due to Baum and Eagon. Experiments on both synthetic and real standard datasets show the effectiveness of our approach. © 2010 IEEE.

Probabilistic Clustering Using the Baum-Eagon Inequality

ROTA BULO', Samuel;PELILLO, Marcello
2010-01-01

Abstract

The paper introduces a framework for clustering data objects in a similarity-based context. The aim is to cluster objects into a given number of classes without imposing a hard partition, but allowing for a soft assignment of objects to clusters. Our approach uses the assumption that similarities reflect the likelihood of the objects to be in a same class in order to derive a probabilistic model for estimating the unknown cluster assignments. This leads to a polynomial optimization in probability domain, which is tackled by means of a result due to Baum and Eagon. Experiments on both synthetic and real standard datasets show the effectiveness of our approach. © 2010 IEEE.
2010
Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/26955
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