On parameters transformations for emulating sparse priors using variational-Laplace inference

03/06/2017 ∙ by Jean Daunizeau, et al. ∙ 0

So-called sparse estimators arise in the context of model fitting, when one a priori assumes that only a few (unknown) model parameters deviate from zero. Sparsity constraints can be useful when the estimation problem is under-determined, i.e. when number of model parameters is much higher than the number of data points. Typically, such constraints are enforced by minimizing the L1 norm, which yields the so-called LASSO estimator. In this work, we propose a simple parameter transform that emulates sparse priors without sacrificing the simplicity and robustness of L2-norm regularization schemes. We show how L1 regularization can be obtained with a "sparsify" remapping of parameters under normal Bayesian priors, and we demonstrate the ensuing variational Laplace approach using Monte-Carlo simulations.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 12

page 14

page 15

page 16

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.