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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)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Efficient First-Order Algorithms for Adaptive Signal Denoising »
Fri Jul 13th 03:40 -- 03:50 PM Room A9
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