Article ID Journal Published Year Pages File Type
10360466 Pattern Recognition 2005 4 Pages PDF
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
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
Preview
Model-based mixture discriminant analysis-an experimental study
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