PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Residual Variance Estimation in Machine Learning
Elia Liitiäinen, Michel Verleysen, Francesco Corona and Amaury Lendasse
Neurocomputing 2009.

Abstract

The problem of residual variance estimation consists of estimating the best possible generalization error obtainable by any model based on a finite sample of data. Even though it is a natural generalization of linear correlation, residual variance estimation in its general form has attracted relatively little attention in machine learning. In this paper, we examine four different residual variance estimators and ana- lyze their properties both theoretically and experimentally to understand better their applicability in machine learning problems. The theoretical treatment differs from previous work by being based on a general formulation of the problem cover- ing also heteroscedastic noise in contrary to previous work, which concentrates on homoscedastic and additive noise. In the second part of the paper, we demonstrate practical applications in input and model structure selection. The experimental results show that using residual variance estimators in these tasks gives good results often with a reduced compu- tational complexity, while the nearest neighbor estimators are simple and easy to implement.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4942
Deposited By:Amaury Lendasse
Deposited On:24 March 2009