Minimum-entropy data partitioning using reversible jump Markovchain Monte Carlo
Roberts, S.J.
Holmes, C.
Denison, D.
Dept. of Eng. Sci., Oxford Univ.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 2001
Volume: 23,
Issue: 8
On page(s): 909-914
ISSN: 0162-8828
References Cited: 18
CODEN: ITPIDJ
INSPEC Accession Number: 7020727
Digital Object Identifier: 10.1109/34.946994
Current Version Published: 2002-08-07
Abstract
Problems in data analysis often require the unsupervised
partitioning of a data set into classes. Several methods exist for such
partitioning but many have the weakness of being formulated via strict
parametric models (e.g., each class is modeled by a single Gaussian) or
being computationally intensive in high-dimensional data spaces. We
reconsider the notion of such cluster analysis in information-theoretic
terms and show that an efficient partitioning may be given via a
minimization of partition entropy. A reversible-jump sampling is
introduced to explore the variable-dimension space of partition models
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