PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the behavior of MDL denoising
Teemu Roos, Petri Myllymäki and Henry Tirri
In: AISTATS 2005, 6-8 Jan 2005, Barbados.

Abstract

We consider wavelet denoising based on minimum description length (MDL) principle. The derivation of an MDL denoising criterion proposed by Rissanen involves a renormalization whose effect on the resulting method has not been well understood so far. By inspecting the behavior of the method we obtain a characterization of its domain of applicability: good performance in the low variance regime but over-fitting in the high variance regime. We also describe unexpected behavior in the theoretical situation where the observed signal is pure noise. An interpretation for the renormalization is given which explains both the empirical and theoretical findings. For practitioners we point out two technical pitfalls and ways to avoid them. Further, we give guidelines for constructing improved MDL denoising methods.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:336
Deposited By:Teemu Roos
Deposited On:12 December 2004