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

Main Authors: Aly Farag, Ayman El-Baz, Refaat Mohamed
Format: Article
Bahasa: eng
Terbitan: , 2007
Subjects:
Logistic regression model
Expectationmaximization
Segmentation
Online Access: https://zenodo.org/record/1076444
ctrlnum 1076444
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language eng
format Journal:Article
Journal
author Aly Farag
Ayman El-Baz
Refaat Mohamed
title Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians
publishDate 2007
topic Logistic regression model
Expectationmaximization
Segmentation
url https://zenodo.org/record/1076444
contents 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.
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