Bayesian L>inf<1>/inf<-Norm Sparse Learning

Yuanqing Lin, D.D. Lee
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings  
We propose a Bayesian framework for learning the optimal regularization parameter in the L 1 -norm penalized leastmean-square (LMS) problem, also known as LASSO [1] or basis pursuit [2] . The setting of the regularization parameter is critical for deriving a correct solution. In most existing methods, the scalar regularization parameter is often determined in a heuristic manner; in contrast, our approach infers the optimal regularization setting under a Bayesian framework. Furthermore, Bayesian
more » ... rthermore, Bayesian inference enables an independent regularization scheme where each coefficient (or weight) is associated with an independent regularization parameter. Simulations illustrate the improvement using our method in discovering sparse structure from noisy data.
doi:10.1109/icassp.2006.1661348 dblp:conf/icassp/LinL06 fatcat:zmpa72tvr5fc5lzpygeh7euv3a