PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Classification EM Algorithm for Binned Data
Allou Samé, Christophe Ambroise and Gérard Govaert
Computational Statistics and Data Analysis Volume 2006, Number 51, pp. 466-480, 2006.

Abstract

A real-time flaw diagnosis application for pressurized containers using acoustic emissions is described. The pressurized containers used are cylindrical tanks containing fluids under pressure. The surface of the pressurized containers is divided into bins, and the number of acoustic signals emanating from each bin is counted. Spatial clustering of high density bins using mixture models is used to detect flaws. A dedicated EM algorithm can be derived to select the mixture parameters, but this is a greedy algorithm since it requires the numerical computation of integrals and may converge only slowly. To deal with this problem, a classification version of theEM(CEM) algorithm is defined, and using synthetic and real data sets, the proposed algorithm is compared to the CEM algorithm applied to classical data. The two approaches generate comparable solutions in terms of the resulting partition if the histogram is sufficiently accurate, but the algorithm designed for binned data becomes faster when the number of available observations is large enough.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6385
Deposited By:Gérard Govaert
Deposited On:08 March 2010