TY - GEN
T1 - Sparse component analysis for linear mixed models
AU - Hurtado, M.
AU - Von Ellenreider, N.
AU - Muravchik, C.
AU - Nehorai, A.
PY - 2010
Y1 - 2010
N2 - When seeking for a sparse solution of a linearmodel, a common technique is the search of a solution with minimum l1 norm. In this paper, we present a new approach for the case of sparse linear mixed models. We combine the EM algorithm for solving the inverse problem with a decision test that guarantees sparseness by eliminating the statistically null components of the solution. We address its performance by means of simulations and illustrate its use with real radar data demonstrating its potential applications.
AB - When seeking for a sparse solution of a linearmodel, a common technique is the search of a solution with minimum l1 norm. In this paper, we present a new approach for the case of sparse linear mixed models. We combine the EM algorithm for solving the inverse problem with a decision test that guarantees sparseness by eliminating the statistically null components of the solution. We address its performance by means of simulations and illustrate its use with real radar data demonstrating its potential applications.
UR - http://www.scopus.com/inward/record.url?scp=78650148248&partnerID=8YFLogxK
U2 - 10.1109/SAM.2010.5606719
DO - 10.1109/SAM.2010.5606719
M3 - Conference contribution
AN - SCOPUS:78650148248
SN - 9781424489770
T3 - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
SP - 137
EP - 140
BT - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
T2 - 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010
Y2 - 4 October 2010 through 7 October 2010
ER -