Generalization of the EM algorithm for mixture density estimation

Authors:
Nasser Kehtarnavaz;Eiji Nakamura
Affiliations:
Department of Electrical Engineering, Texas A & M University, College Station, TX 77843, USA;Department of Information Network Engineering, Aichi Institute of Technology, Toyota, Aichi, 47003, Japan
Venue:
Pattern Recognition Letters
Year:
1998

Citing 1
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Abstract

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.