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### Local Minimax Learning of Approximately Polynomial Functions

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### Abstract

Suppose we have a number of noisy measurements of an unknown real-valued function \$f\$ near point of interest \$mathbf{x}_0 in mathbb{R}^d\$. Suppose also that nothing can be assumed about the noise distribution, except for zero mean and bounded covariance matrix. We want to estimate \$f\$ at \$mathbf{x=x}_0\$ using a general linear parametric family \$f(mathbf{x};mathbf{a}) = a_0 h_0 (mathbf{x}) ++ a_q h_q (mathbf{x})\$, where \$mathbf{a} in mathbb{R}^q\$ and \$h_i\$'s are bounded functions on a neighborhood \$B\$ of \$mathbf{x}_0\$ which contains all points of measurement. Typically, \$B\$ is a Euclidean ball or cube in \$mathbb{R}^d\$ (more generally, a ball in an \$l_p\$-norm). In the case when the \$h_i\$'s are polynomial functions in \$x_1,ldots,x_d\$ the model is called locally-polynomial. In particular, if the \$h_i\$'s form a basis of the linear space of polynomials of degree at most two, the model is called locally-quadratic (if the degree is at most three, the model is locally-cubic, etc.). Often, there is information, which is called context, about the function \$f\$ (restricted to \$B\$ ) available, such as that it takes values in a known interval, or that it satisfies a Lipschitz condition. The theory of local minimax estimation with context for locally-polynomial models and approximately locally polynomial models has been recently initiated by Jones. In the case of local linearity and a bound on the change of \$f\$ on \$B\$, where \$B\$ is a ball, the solution for squared error loss is in the form of ridge regression, where the ridge parameter is identified; hence, minimax justification for ridge regression is given together with explicit best error bounds. The analysis of polynomial models of degree above 1 leads to interesting and difficult questions in real algebraic geometry and non-linear optimization. We show that in the case when \$f\$ is a probability function, the optimal (in the minimax sense) estimator is effectively computable (with any given precision), thanks to Tarski's elimination principle.

### BibTeX - Entry

```@InProceedings{jones_et_al:DSP:2007:891,
author =	{Lee Jones and Konstantin Rybnikov},
title =	{Local Minimax Learning of Approximately Polynomial Functions},
booktitle =	{Combinatorial and Algorithmic Foundations of Pattern and Association Discovery},
year =	{2007},
editor =	{Rudolf Ahlswede and Alberto Apostolico and Vladimir I. Levenshtein},
number =	{06201},
series =	{Dagstuhl Seminar Proceedings},
ISSN =	{1862-4405},
publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
address =	{Dagstuhl, Germany},
URL =		{http://drops.dagstuhl.de/opus/volltexte/2007/891},
annote =	{Keywords: Local learning, statistical learning, estimator, minimax, convex optimization, quantifier elimination, semialgebraic, ridge regression, polynomial}
}
```

 Keywords: Local learning, statistical learning, estimator, minimax, convex optimization, quantifier elimination, semialgebraic, ridge regression, polynomial Seminar: 06201 - Combinatorial and Algorithmic Foundations of Pattern and Association Discovery Issue date: 2007 Date of publication: 13.02.2007

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