Jacobi Alternative to Bayesian Evidence Maximization in Diffusion Filtering
Ramunas Girdziusas and Jorma Laaksonen
In: 15th International Conference on Artificial Neural Networks, 11-15 Sep 2005, Warsaw, Poland.

Abstract

Nonlinear diffusion filtering presents a way to define and iterate Gaussian process regression so that large variance noise can be efficiently filtered from observations of size n in m iterations by performing approximately O(mn) number of multiplications, while at the same time preserving the edges of the signal. Experimental evidence indicates that the optimal stopping time exist and the steady state solutions obtained by setting m to an arbitrarily large number are suboptimal. This work discusses the Bayesian evidence criterion, gives an interpretation to its basic components and proposes an alternative, simple optimal stopping method. A synthetic large-scale example indicates the usefulness of the proposed stopping criterion.

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