Sciweavers

Share
ICASSP
2011
IEEE

Fast adaptive variational sparse Bayesian learning with automatic relevance determination

7 years 11 months ago
Fast adaptive variational sparse Bayesian learning with automatic relevance determination
In this work a new adaptive fast variational sparse Bayesian learning (V-SBL) algorithm is proposed that is a variational counterpart of the fast marginal likelihood maximization approach to SBL. It allows one to adaptively construct a sparse regression or classification function as a linear combination of a few basis functions by minimizing the variational free energy. In the case of non-informative hyperpriors, also referred to as automatic relevance determination, the minimization of the free energy can be efficiently realized by computing the fixed points of the update expressions for the variational distribution of the sparsity parameters. The criteria that establish convergence to these fixed points, termed pruning conditions, allow an efficient addition or removal of basis functions; they also have a simple and intuitive interpretation in terms of a component’s signal-to-noise ratio. It has been demonstrated that this interpretation allows a simple empirical adjustment o...
Dmitriy Shutin, Thomas Buchgraber, Sanjeev R. Kulk
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Dmitriy Shutin, Thomas Buchgraber, Sanjeev R. Kulkarni, H. Vincent Poor
Comments (0)
books