Maximum a Posteriori Based Regularization Parameter Selection
Paper i proceeding, 2011

The l(1) norm regularized least square technique has been proposed as an efficient method to calculate sparse solutions. However, the choice of the regularization parameter is still an unsolved problem, especially when the number of nonzero elements is unknown. In this paper we first design different ML estimators by interpreting the l(1) norm regularization as a MAP estimator with a Laplacian model for data. We also utilize the MDL criterion to decide on the regularization parameter. The performance of these new methods are evaluated in the context of estimating the Directions Of Arrival (DOA) for the simulated data and compared. The simulations show that the performance of the different forms of the MAP estimator are approximately equal in the one snapshot case, where MDL may not work. But for the multiple snapshot case both methods can be used.

DOA estimation

selection

principle

Linear regression

Sparse analysis

LASSO

Model order

model-order selection

lasso

Författare

Ashkan Panahi

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Mats Viberg

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

2452-2455 5946980

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1109/ICASSP.2011.5946980

ISBN

978-1-4577-0539-7