EM_MVGM 1.0

Date Added: June 21, 2013 | Visits: 271

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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 ------- [logl , M , S , P] = em_mvgm(Z , M0 , S0 , P0 , [nbite]); Inputs ------- Z Measurements (m x K x [n1] x ... x [nl]) M0 Initial mean vector. M0 can be (d x 1 x p x [v1] x ... x [vr]) S0 Initial covariance matrix. S0 can be (d x d 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 ------- 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

Requirements: No special requirements

Platforms: Matlab

Keyword: Covariance, Default, Final, Initial, Iteration, Matrix, Number, Outputs, Probabilities, Vector

Users rating: 0/10

License: Shareware

Size: 20.48 KB

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