Title :
Linear Regression with a Sparse Parameter Vector
Author :
Larsson, Erik G. ; Selén, Yngve
Author_Institution :
Sch. of EE, Commun. Theory, R. Inst. of Technol., Stockholm
Abstract :
We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive a computationally efficient approximation to the minimum mean-square error (MMSE) estimate of the parameter vector. The performance of the so-obtained estimate is illustrated via numerical examples
Keywords :
Bayes methods; least mean squares methods; regression analysis; signal processing; Bayesian framework; MMSE estimate; linear regression; minimum mean-square error estimate; sparse parameter vector; Bayesian methods; Councils; Gaussian noise; Information technology; Linear regression; Maximum likelihood estimation; Parameter estimation; Vectors; Virtual reality; White noise;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1660652
