The code file for paper "Robust Bayesian Matrix Decomposition with Mixture of Gaussian Noise". The main function is BMD_MoG
function [label,model,OutU,OutV,llh,converged,t] = BMD_MoG(InX,r,param)
% function [label,model,OutU,OutV,llh,converged,t] = BMD_MoG(InX,r,param)
% Perform EM algorithm for fitting the BMD_MoG model.
% Step 1: Max parameters;
% Step 2: Expectation;
% Step 3: Max weighted L2 MF;
% Step 4: Expectation.
%Input:
% InX: d x n input data matrix
% r: the rank
% param.maxiter: maximal iteration number
% param.OriX: ground truth matrix
% param.InU,InV: Initialized factorized matrices
% param.k: the number of GMM
% param.display: display the iterative process
% param.tol: the thresholding for stop
%Output:
% label:the labels of the noises
% model:model.mu, the means of the different Gaussians
% model.Sigma,the variance of the different Gaussians,sigma^2
% model.weight,the mixing coefficients
% model.alpha,the parameter of Dirichlet prior on V
% model.lambda,the parameter of Laplace prior on U
% W: d x n weighted matrix
% OutU: the fianl factorized matrix U
% OutV: the fianl factorized matrix V
% llh: the log likelihood
%