Hybrid approximate message passing for generalized group sparsity

Alyson K. Fletcher; Sundeep Rangan

doi:10.1117/12.2026729

26 September 2013 Hybrid approximate message passing for generalized group sparsity

Alyson K. Fletcher, Sundeep Rangan

Proceedings Volume 8858, Wavelets and Sparsity XV; 88580P (2013) https://doi.org/10.1117/12.2026729
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

We consider the problem of estimating a group sparse vector x ∈ Rⁿ under a generalized linear measurement model. Group sparsity of x means the activity of different components of the vector occurs in groups - a feature common in estimation problems in image processing, simultaneous sparse approximation and feature selection with grouped variables. Unfortunately, many current group sparse estimation methods require that the groups are non-overlapping. This work considers problems with what we call generalized group sparsity where the activity of the different components of x are modeled as functions of a small number of boolean latent variables. We show that this model can incorporate a large class of overlapping group sparse problems including problems in sparse multivariable polynomial regression and gene expression analysis. To estimate vectors with such group sparse structures, the paper proposes to use a recently-developed hybrid generalized approximate message passing (HyGAMP) method. Approximate message passing (AMP) refers to a class of algorithms based on Gaussian and quadratic approximations of loopy belief propagation for estimation of random vectors under linear measurements. The HyGAMP method extends the AMP framework to incorporate priors on x described by graphical models of which generalized group sparsity is a special case. We show that the HyGAMP algorithm is computationally efficient, general and offers superior performance in certain synthetic data test cases.

Citation Download Citation

Alyson K. Fletcher and Sundeep Rangan "Hybrid approximate message passing for generalized group sparsity", Proc. SPIE 8858, Wavelets and Sparsity XV, 88580P (26 September 2013); https://doi.org/10.1117/12.2026729

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