Abstract
We consider a fully Bayesian treatment of radial basis function regression, and propose a solution to the the instability of basis selection. Indeed, when bases are selected solely according to the magnitude of their posterior inclusion probabilities, it is often the case that many bases in the same neighborhood end up getting selected leading to redundancy and ultimately inaccuracy of the representation. In this paper, we propose a straightforward solution to the problem based on post-processing the sample path yielded by the model space search technique. Specifically, we perform an a posteriori model-based clustering of the sample path via a mixture of Gaussians, and then select the points closer to the means of the Gaussians. Our solution is found to be more stable and yields a better performance on simulated and real tasks.
Publication Date
7-2011
Document Type
Technical Report
Department, Program, or Center
The John D. Hromi Center for Quality and Applied Statistics (KGCOE)
Recommended Citation
Fokoue, E. (2011). Stable radial basis function selection via mixture modelling of the sample path. Journal of Data Science 9(3), 359-372.
Campus
RIT – Main Campus
