IEEE Trans Pattern Anal Mach Intell - Robust Recovery of Corrupted Low-rank Matrix by Implicit Regularizers.

Tópicos

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Resumo

Low-rank matrix recovery algorithms aim to recover a corrupted low-rank matrix with sparse errors. However, corrupted errors may not be sparse in real-world problems and the relationship between L1 regularizer on noise and robust M-estimators is still unknown. This paper proposes a general robust framework for low-rank matrix recovery via implicit regularizers of robust M-estimators, which are derived from convex conjugacy and can be used to model arbitrarily corrupted errors. Based on the additive form of half-quadratic optimization, proximity operators of implicit regularizers are developed such that both low-rank structure and corrupted errors can be alternately recovered. In particular, the dual relationship between the absolute function in L1 regularizer and Huber M-estimator is studied, which establishes a relationship between robust low-rank matrix recovery methods and M-estimators based robust principal component analysis methods. Extensive experiments on synthetic and real-world datasets corroborate our claims and verify the robustness of the proposed framework.

Resumo Limpo

lowrank matrix recoveri algorithm aim recov corrupt lowrank matrix spars error howev corrupt error may spars realworld problem relationship l regular nois robust mestim still unknown paper propos general robust framework lowrank matrix recoveri via implicit regular robust mestim deriv convex conjugaci can use model arbitrarili corrupt error base addit form halfquadrat optim proxim oper implicit regular develop lowrank structur corrupt error can altern recov particular dual relationship absolut function l regular huber mestim studi establish relationship robust lowrank matrix recoveri method mestim base robust princip compon analysi method extens experi synthet realworld dataset corrobor claim verifi robust propos framework

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