The following Matlab project contains the source code and Matlab examples used for recovery of low rank and sparse matrix.
This code solves the problem of recovering a low rank and sparse(in transform domain)matrix from its lower dimensional projections
Minimize (lambda1)||X||* + (lambda2)||Dx||_1 + 1/2 || A(X) - y ||_2^2
Formulated as an unconstarined nuclear norm and L1 minimization problem using Split bregman algorithm, formulation for the problem is as follows
% Minimize (lambda1)||W||* + (lambda2)||Dz||_1 + 1/2 || A(X) - y ||_2^2 + eta1/2 || W-X-B1 ||_2^2 +eta2/2 || W-Z-B2 ||_2^2
%W, Z are auxillary variable and B1, B2 are the bregman variable
Use of split bregman technique helps improve the accuracy and convergence behavior of the algorithm.