OREGON STATE UNIVERSITY

You are here

Tuning-free joint sparse recovery via optimization transfer

TitleTuning-free joint sparse recovery via optimization transfer
Publication TypeConference Paper
Year of Publication2012
AuthorsChunikhina, E., G. Gutshall, R. Raich, and T. Nguyen
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pagination1913 - 1916
Date Published03/2012
PublisherIEEE
Conference LocationKyoto, Japan
ISBN Number978-1-4673-0044-5
Keywordsjoint sparsity, MMV, multiple-measurement vector, optimization transfer, sparse representation
Abstract

Multiple measurement vector (MMV) problem addresses the recovery of a set of sparse vectors that have common sparsity pattern. In this paper, we consider a variant of the MMV problem where the common sparsity pattern is obfuscated by an additive noise. Specifically, we study the conditions for perfect reconstruction of the original sparsity pattern. Based on these, we develop a tuning-free algorithm for recovering jointly sparse solutions via the transfer optimization approach. We provide a preliminary numerical evaluation to illustrate our approach.

DOI10.1109/ICASSP.2012.6288278