Efficient reconstruction of block-sparse signals

Joel Goodman, Keith Forsythe, Benjamin A. Miller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N M K. The exact basis vectors, however, are not known a priori and must be chosen from an M N matrix. Such under-determined problems can be solved using an 2 optimization with an 1 penalty on the sparsity of the solution. There are practical applications in which multiple measurements can be grouped together, so that K P data must be estimated from M P observations, where the 1 sparsity penalty is taken with respect to the vector formed using the 2 norms of the rows of the data matrix. In this paper we develop a computationally efficient block partitioned ho-motopy method for reconstructing K P data from M P observations using a grouped sparsity constraint, and compare its performance to other block reconstruction algorithms.

Original languageEnglish
Title of host publication2011 IEEE Statistical Signal Processing Workshop, SSP 2011
Number of pages4
StatePublished - 2011
Externally publishedYes
Event2011 IEEE Statistical Signal Processing Workshop, SSP 2011 - Nice, France
Duration: 28 Jun 201130 Jun 2011

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


Conference2011 IEEE Statistical Signal Processing Workshop, SSP 2011

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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