Keywords: [ Models and Methods ] [ Learning on Graphs ]

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Abstract
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Oral presentation:
Learning Theory

Thu 15 Apr 3:15 p.m. PDT — 4:15 p.m. PDT

Tue 13 Apr 2 p.m. PDT — 4 p.m. PDT

Thu 15 Apr 3:15 p.m. PDT — 4:15 p.m. PDT

Abstract:
In this paper we study the statistical properties of Laplacian smoothing, a graph-based approach to nonparametric regression. Under standard regularity conditions, we establish upper bounds on the error of the Laplacian smoothing estimator \smash{$\widehat{f}$}, and a goodness-of-fit test also based on \smash{$\widehat{f}$}. These upper bounds match the minimax optimal estimation and testing rates of convergence over the first-order Sobolev class $H^1(\mathcal{X})$, for $\mathcal{X} \subseteq \mathbb{R}^d$ and $1 \leq d < 4$; in the estimation problem, for $d = 4$, they are optimal modulo a $\log n$ factor. Additionally, we prove that Laplacian smoothing is manifold-adaptive: if $\mathcal{X} \subseteq \mathbb{R}^d$ is an $m$-dimensional manifold with $m < d$, then the error rate of Laplacian smoothing (in either estimation or testing) depends only on $m$, in the same way it would if $\mathcal{X}$ were a full-dimensional set in $\mathbb{R}^m$.

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