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.
% 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
% S - sparse component
% L - low rank component
requires sparco for defining operators
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