Local Feature Selection for the Relevance Vector Machine Using Adaptive Kernel Learning

Abstract

A Bayesian learning algorithm is presented that is based on a sparse Bayesian linear model (the Relevance Vector Machine (RVM)) and learns the parameters of the kernels during model training. The novel characteristic of the method is that it enables the introduction of parameters called ‘scaling factors’ that measure the significance of each feature. Using the Bayesian framework, a sparsity promoting prior is then imposed on the scaling factors in order to eliminate irrelevant features. Feature selection is local, because different values are estimated for the scaling factors of each kernel, therefore different features are considered significant at different regions of the input space. We present experimental results on artificial data to demonstrate the advantages of the proposed model and then we evaluate our method on several commonly used regression and classification datasets.