CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Maximum a Posteriori Based Regularization Parameter Selection

Ashkan Panahi (Institutionen för signaler och system, Signalbehandling) ; Mats Viberg (Institutionen för signaler och system, Signalbehandling)
2011 Ieee International Conference on Acoustics, Speech, and Signal Processing (1520-6149). p. 2452-2455. (2011)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Linear regression, Sparse analysis, DOA estimation, LASSO, Model order, selection, model-order selection, principle, lasso



Denna post skapades 2011-12-22. Senast ändrad 2015-07-02.
CPL Pubid: 150882

 

Läs direkt!

Lokal fulltext (fritt tillgänglig)

Länk till annan sajt (kan kräva inloggning)


Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Information Technology

Chalmers infrastruktur

Relaterade publikationer

Denna publikation ingår i:


Parameter Estimation Using Sparse Modeling: Algorithms and Performance Analysis