Fast Marginal Likelihood Maximisation for Sparse Bayesian Models
Michael E. Tipping, Anita Faul · 2003
The 'sparse Bayesian' modelling approach, as exemplified by the 'relevance vector machine ', enables sparse classification and regression functions to be obtained by linearlyweighting a small nmnber of fixed basis functions from a large dictionary of potential candidates. Such a model conveys a nmnber of advantages over the related and very popular 'support vector machine', but the necessary 'training' procedure optimisation of the marginal likelihood function is typically much slower. We describe a new and highly accelerated algorithm which exploits recently-elucidated properties of the marginal likelihood function to enable maximisation via a principled and efficient sequential addition and deletion of candidate basis functions.