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Efficient First-Order Algorithms for Adaptive Signal Denoising
Dmitrii Ostrovskii · Zaid Harchaoui

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #51

We consider the problem of discrete-time signal denoising, focusing on a specific family of non-linear convolution-type estimators. Each such estimator is associated with a time-invariant filter which is obtained adaptively, by solving a certain convex optimization problem. Adaptive convolution-type estimators were demonstrated to have favorable statistical properties, see (Juditsky & Nemirovski, 2009; 2010; Harchaoui et al., 2015b; Ostrovsky et al., 2016). Our first contribution is an efficient implementation of these estimators via the known first-order proximal algorithms. Our second contribution is a computational complexity analysis of the proposed procedures, which takes into account their statistical nature and the related notion of statistical accuracy. The proposed procedures and their analysis are illustrated on a simulated data benchmark.

Author Information

Dmitrii Ostrovskii (INRIA)
Zaid Harchaoui (University of Washington)

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