EM_MVGM

Fast implementation of EM algorithm for multivariate gaussian mixture

Mex implementation of EM algorithm for multivariate Gaussian mixture. Multiple data/initial parameters are allowed by ND slices definition

em_mvgm : Expectation-Maximization algorithm for Multivariate Gaussian Mixtures

Usage
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[logl , M , S , P] = em_mvgm(Z , M0 , S0 , P0 , [nbite]);

Inputs
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Z Measurements (m x K x [n1] x ... x [nl])
M0 Initial mean vector. M0 can be (m x 1 x p x [v1] x ... x [vr])
S0 Initial covariance matrix. S0 can be (m x m x p x [v1] x ... x [vr])
P0 Initial mixture probabilities (1 x 1 x p) : P0 can be (1 x 1 x d x [v1] x ... x [vr])
nbite Number of iteration (default = 10)

Outputs
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logl Final loglikelihood (n1 x ... x nl x v1 x ... x vr)
M Estimated mean vector (d x 1 x p x n1 x ... x nl v1 x ... x vr)
S Estimated covariance vector (d x d x p x n1 x ... x nl v1 x ... x vr)
P Estimated initial probabilities (1 x 1 x p x n1 x ... x nl v1 x ... x vr)

Please run mexme_em_mvgm for compile mex file on your own systems.

Run test_em_mvgm.m for a demo