2000 character limit reached
Compressed Sensing for Block-Sparse Smooth Signals
Published 10 Sep 2013 in stat.ML, cs.IT, math.IT, math.ST, and stat.TH | (1309.2505v1)
Abstract: We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion. We develop low-complexity solvers for our proposed formulations through the alternating direction method of multipliers.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
