Title :
The expectation-maximization algorithm: Gaussian case
Author_Institution :
Dept. of Math. & Comput. Sci., Tech. Univ. of Civil Eng., Bucharest, Romania
Abstract :
There are some situations when in the pattern recognition applications can appear some objects which are missing data. This thing one happens since the process of data acquisition isn´t perfect. In this paper we shall present the EM algorithm (Expectation Maximization) which is used in order to estimate the parameters corresponding to a probability density function when we dispose by missing data. In our case, the class labels are the missing data.
Keywords :
Gaussian processes; expectation-maximisation algorithm; pattern clustering; EM algorithm; expectation-maximization algorithm; mixturealgorithm; parameter estimation; pattern recognition; probability density function; Data acquisition; Parameter estimation; Pattern recognition; Probability density function; Expectation-Maximization algorithm; a posteriori probability; gaussian mixture; missing data; probability density function;
Conference_Titel :
Networking and Information Technology (ICNIT), 2010 International Conference on
Conference_Location :
Manila
Print_ISBN :
978-1-4244-7579-7
DOI :
10.1109/ICNIT.2010.5508443
