Model-based mixture discriminant analysis - An experimental study

Zohar Halbe; Mayer Aladjem

doi:10.1016/j.patcog.2004.08.010

Model-based mixture discriminant analysis - An experimental study

Zohar Halbe, Mayer Aladjem

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.

Original language	English
Pages (from-to)	437-440
Number of pages	4
Journal	Pattern Recognition
Volume	38
Issue number	3
DOIs	https://doi.org/10.1016/j.patcog.2004.08.010
State	Published - 1 Mar 2005

Keywords

Density estimation
Discriminant analysis
Gaussian mixture model
Model selection

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10.1016/j.patcog.2004.08.010

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title = "Model-based mixture discriminant analysis - An experimental study",

abstract = "The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.",

keywords = "Density estimation, Discriminant analysis, Gaussian mixture model, Model selection",

author = "Zohar Halbe and Mayer Aladjem",

note = "Funding Information: This work was supported in part by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University of the Negev, Israel.",

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AU - Aladjem, Mayer

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N2 - The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.

AB - The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.

KW - Density estimation

KW - Discriminant analysis

KW - Gaussian mixture model

KW - Model selection

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EP - 440

JO - Pattern Recognition

JF - Pattern Recognition

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Model-based mixture discriminant analysis - An experimental study

Abstract

Keywords

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