Publication
We introduce a new convex formulation for stable principal component pursuit (SPCP) to decompose noisy signals into low-rank and sparse representations. For numerical solutions of our SPCP formulation, we first develop a convex variational framework and then accelerate it with quasi-Newton methods. We show, via synthetic and real data experiments, that our approach offers advantages over the classical SPCP formulations in scalability and practical parameter selection.
Mario Paolone, André Hodder, Lucien André Félicien Pierrejean, Simone Rametti
Giuseppe Carleo, Riccardo Rossi, Clemens Giuliani, Filippo Vicentini