TY - GEN

T1 - Efficient reconstruction of block-sparse signals

AU - Goodman, Joel

AU - Forsythe, Keith

AU - Miller, Benjamin A.

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80052256182&partnerID=8YFLogxK

U2 - 10.1109/SSP.2011.5967779

DO - 10.1109/SSP.2011.5967779

M3 - Conference contribution

AN - SCOPUS:80052256182

SN - 9781457705700

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 629

EP - 632

BT - 2011 IEEE Statistical Signal Processing Workshop, SSP 2011

T2 - 2011 IEEE Statistical Signal Processing Workshop, SSP 2011

Y2 - 28 June 2011 through 30 June 2011

ER -