We apply the Majorization Minimization technique to solve the problem OF L1 -norm based low rank matrix factorization, at each iteration, we upper bound the original function with a strongly convex surrogate.
A globally variance-constrained sparse representation (GVCSR) model is proposed in this paper, where a variance-constrained rate term is introduced to the optimization process.