Recovery of low rank and joint Sparse matrix using Split Bregman
This work deals with recovering a low rank and joint sparse matrix from its lower dimensional projections via nuclear norm and L21 minimization.
% Minimize ||X||*+||DX||2,1 (nuclear norm +l21 norm)
% Subject to A(X) = Y
We use split Bregman algorithm for the same.
% Minimize 1/2||y-Ax||^2 +lambda1||W||* +lambda2||DZ||2,1 +eta1/2||W-X-B1||^2
+eta2/2||Z-X-B2||^2
%W and Z are proxy variables
B1 and B2 are the Bregman variables
The use of Bregman technique improves the convergence and the accuracy of reconstruction.
Cite As
Ankita (2024). Recovery of low rank and joint Sparse matrix using Split Bregman (https://www.mathworks.com/matlabcentral/fileexchange/45129-recovery-of-low-rank-and-joint-sparse-matrix-using-split-bregman), MATLAB Central File Exchange. Retrieved .
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- Test and Measurement > Instrument Control Toolbox >
- Sciences > Physics > Particle & Nuclear Physics >
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Version | Published | Release Notes | |
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1.1.0.0 | Updated Description |