DocumentCode :
3420569
Title :
A new approach to constrained expectation-maximization for density estimation
Author :
Hong, Hunsop ; Schonfeld, Dan
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Illinois at Chicago, Chicago, IL
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
3689
Lastpage :
3692
Abstract :
In this paper, we present two density estimation methods based on constrained expectation-maximization (EM) algorithm. We propose a penalty-based maximum-entropy expectation-maximization (MEEM) algorithm to obtain a smooth estimate of the density function. We further propose an attraction-repulsion expectation- maximization (AREM) algorithm for density estimation in order to determine equilibrium between over-smoothing and over-fitting of the estimated density function. Computer simulation results are used to show the effectiveness of the proposed constrained expectation- maximization algorithms in image reconstruction and sensor field estimation from randomly scattered samples.
Keywords :
expectation-maximisation algorithm; image reconstruction; maximum entropy methods; attraction-repulsion expectation- maximization; density estimation; density function; image reconstruction; maximum-entropy expectation-maximization; sensor field estimation; Computer simulation; Covariance matrix; Density functional theory; Entropy; Image reconstruction; Image sensors; Iterative algorithms; Kernel; Probability density function; Scattering; Gaussian mixture model (GMM); Gibbs density function; expectation-maximization (EM); image reconstruction; maximum entropy penalty; sensor field estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518453
Filename :
4518453
Link To Document :
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