Mixture density estimation with group membership functions

Authors:
Xiangyu Yang;Jun Liu
Affiliations:
Information System Research Lab, S2-B3a-06, School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, Singapore;Information System Research Lab, S2-B3a-06, School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, Singapore
Venue:
Pattern Recognition Letters
Year:
2002

Citing 3
Cited 2

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Generalization of the EM algorithm for mixture density estimation

Pattern Recognition Letters
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A fully Bayesian model based on reversible jump MCMC and finite Beta mixtures for clustering

Expert Systems with Applications: An International Journal

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Abstract

The mixture density model has been extensively studied in the field of statistical pattern recognition. And the EM algorithm has been well known as a convenient and efficient tool to iteratively compute the maximum likelihood estimates of mixture model parameters (except the number of components in mixture). However, it is a difficult problem to estimate the number of components in mixture. In order to solve this problem, we present a new mixture density model based on the concept of group, which is defined as a pair of different components. With the new mixture density model, it is possible for two components to have the same component parameters, so the accurate estimation of the number of components is no longer needed. Based on the new mixture density model, the mixture density estimation method with group membership functions is derived, and it is demonstrated to be an efficient method in the experiments of normal mixture density estimations.