Global optimization of the generalized cross-validation criterion

Authors:
J. T. Kent;M. Mohammadzadeh
Affiliations:
Department of Statistics, University of Leeds, Leeds, LS2 9JT, England;Department of Statistics, Tarbiat Modarres University, P.O. Box 14155-4838, Tehran, Iran
Venue:
Statistics and Computing
Year:
2000

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New computer methods for global optimization

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A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Generalized cross-validation is a method for choosing the smoothing parameter in smoothing splines and related regularization problems. This method requires the global minimization of the generalized cross-validation function. In this paper an algorithm based on interval analysis is presented to find the globally optimal value for the smoothing parameter, and a numerical example illustrates the performance of the algorithm.