Graduate School and Research Center in Digital Sciences

Save - Space alternating variational estimation for sparse Bayesian learning

Kurisummoottil Thomas, Christo; Slock, Dirk TM

DSW 2018, IEEE Data Science Workshop, June 4-6, 2018, Lausanne, Switzerland

In this paper, we address the fundamental problem of sparse signal recovery in a Bayesian framework. The computational complexity associated with Sparse Bayesian Learning (SBL) renders it infeasible even for moderately large problem sizes. To address this issue, we propose a fast version of SBL using Variational Bayesian (VB) inference. VB allows one to obtain analytical approximations to the posterior distributions of interest even when exact inference of these distributions is intractable. We propose a novel fast algorithm called space alternating variational estimation (SAVE), which is a version of VB(-SBL) pushed to the scalar level. Similarly as for SAGE (space-alternating generalized expectation maximization) compared to EM, the component-wise approach of SAVE compared to SBL renders it less likely to get stuck in bad local optima and its inherent damping (more cautious progression) also leads to typically faster convergence of the non-convex optimization process. Simulation results show that the proposed algorithm has a faster convergence rate and achieves lower MSE than other state of the art fast SBL methods.

Document Doi Bibtex

Title:Save - Space alternating variational estimation for sparse Bayesian learning
Keywords:Sparse Bayesian Learning, Variational Bayes, Approximate Message Passing, Alternating Optimization
Department:Communication systems
Eurecom ref:5543
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Bibtex: @inproceedings{EURECOM+5543, doi = {}, year = {2018}, title = {{S}ave - {S}pace alternating variational estimation for sparse {B}ayesian learning}, author = {{K}urisummoottil {T}homas, {C}hristo and {S}lock, {D}irk {TM}}, booktitle = {{DSW} 2018, {IEEE} {D}ata {S}cience {W}orkshop, {J}une 4-6, 2018, {L}ausanne, {S}witzerland}, address = {{L}ausanne, {SWITZERLAND}}, month = {06}, url = {} }
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