Title :
Non-parametric expectation maximization: a learning automata approach
Author :
Abd-Almageed, Wael ; Osery, Aly Ei ; Smith, Christopher E.
Author_Institution :
Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA
Abstract :
The famous expectation maximization technique suffers two major drawbacks. First, the number of components has to be specified apriori. Also, the expectation maximization is sensitive to initialization. In this paper, we present a new stochastic technique for estimating the mixture parameters. Parzen Window is used to estimate a discrete estimate of the PDF of the given data. Stochastic learning automata is then used to select the mixture parameters that minimize the distance between the discrete estimate of the PDF and the estimate of the expectation maximization. The validity of the proposed approach is verified using bivariate simulation data.
Keywords :
learning automata; nonparametric statistics; optimisation; parameter estimation; probability; stochastic processes; PDF; Parzen Window; bivariate simulation data; expectation maximization technique; mixture parameters estimation; nonparametric expectation maximization; probability density function; stochastic learning automata; stochastic technique; Automatic speech recognition; Computational modeling; Computer vision; Density functional theory; Learning automata; Parameter estimation; Probability density function; Speech processing; Speech recognition; Stochastic processes;
Conference_Titel :
Systems, Man and Cybernetics, 2003. IEEE International Conference on
Print_ISBN :
0-7803-7952-7
DOI :
10.1109/ICSMC.2003.1244347
