EM algorithm for Gaussian mixture model 1.0

This is a function performs maximum likelihood estimation of Gaussian mixture model by using expectation maximization algorithm.It can work on data of arbitrary dimensions. Several techniques are applied in order to avoid the float number underflow problems that often occurs on applying probability analysis on high dimensional data. Speed is another major concern which is optimized by vertorization and matrix factorization.This is a widely used algorithm. The detail of this algorithm can be found in any textbook or tutorial. Just google EM Gaussian Mixture or you can find it herehttp://en.wikipedia.org/wiki/Expectation-maximization_algorithmThis function is robust and speedy yet the code structure is very clear. The code is easy to read.example:load data;label = emgm(x,3);spread(x,label);

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Detail,  Dimensional,  Factorization,  Matrix,  Optimized,  Speed,  Vertorization,  Widely
Users rating:	0/10