Bayesian L1-Norm Sparse Learning

We propose a Bayesian framework for learning the optimal regularization parameter in the L₁-norm penalized least-mean-square (LMS) problem, also known as LASSO (R. Tibshirani, 1996) or basis pursuit (S.S. Chen et al., 1998). 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 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