Generalized Principal Component Pursuit

min nuclear_norm(L) + beta*||W(S)||_1 subject to ||y-F(S+L)|_2 < err
1.4K Downloads
Updated 9 Sep 2010

View License

This is a generalized version of Principal Component Pursuit (PCP) where the sparsity is assumed in a transform domain and not in measurement domain. Moreover the samples obtained are lower dimensional projections.
% Inputs
% y - observation (lower dimensional projections)
% F - projection from signal domain to observation domain
% W - transform where the signal is sparse
% beta - term balancing sparsity and rank deficiency

% Outputs
% S - sparse component
% L - low rank component

requires sparco for defining operators
http://www.cs.ubc.ca/labs/scl/sparco/

Cite As

Angshul Majumdar (2024). Generalized Principal Component Pursuit (https://www.mathworks.com/matlabcentral/fileexchange/28677-generalized-principal-component-pursuit), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes
1.0.0.0