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Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians

By Aly Farag, Ayman El-baz and Refaat Mohamed

Abstract

Abstract — In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach. Keywords—Logistic regression model, Expectation maximization, Segmentation

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.193.1932
Provided by: CiteSeerX
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