Weighted methods in sparse recovery

TitleWeighted methods in sparse recovery
Publication TypePresentation
Year of Publication2012
AuthorsOzgur Yilmaz
KeywordsPresentation, SINBAD, SINBADFALL2012, SLIM

In the recent years we have successfully employed "weighted" algorithms to recover sparse signals from few linear, non-adaptive measurements. The general principle here is to use prior knowledge about the signal to be recovered, e.g., approximate locations of large-in-magnitude transform coefficients, if such information is available. An example for this is the use of weighted 1-norm minimization to improve wavefield reconstruction from randomized (sub)sampling. We will review these results and outline some new directions we have explored during the last year, such as weighted non-convex sparse recovery (see Ghadermarzy's talk), weighted analysis-based recovery (see Hargreaves's talk), and a weighted randomized Kaczmarz algorithm for solving large overdetermined systems of equations that are known to admit a (nearly) sparse solution. Various examples in seismic will be shown.

Citation Keyyilmaz2012SINBADwms