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Generalization of the EM algorithm for mixture density estimation

delete1998-02-01
delete22
PRE
AI
N
Nasser Kehtarnavaz
E
E. Nakamura
DOI：10.1016/S0167-8655(97)00173-6delete
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Abstract

Abstract

En 中文
The expectation-maximization (EM) algorithm is used for estimating mixture density parameters. This algorithm relies on the assumption that the number of component densities is given or known. This paper presents a preprocessing module to generalize the EM algorithm for the purpose of easing the assumption regarding the number of component densities. This module consists of a clustering algorithm, called multi-scale clustering, which allows an optimal number of component densities to be found by using scale-space theory. Examples are provided to (i) illustrate the improvement made by this generalization over the original EM algorithm and (ii) examine the performance of the developed algorithm in realistic situations. (C) 1998 Elsevier Science B.V. All rights reserved.
Keywords:
mixture density estimation
expectation-maximization
model selection
multi-scale clustering
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Journal

Pattern Recognition Letters cover
Pattern Recognition Letters
IF:
3.3
Papers:
7.9K
Citations:
1.6W

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