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June 2020 Local nearest neighbour classification with applications to semi-supervised learning
Timothy I. Cannings, Thomas B. Berrett, Richard J. Samworth
Ann. Statist. 48(3): 1789-1814 (June 2020). DOI: 10.1214/19-AOS1868


We derive a new asymptotic expansion for the global excess risk of a local-$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the test point. This expansion elucidates conditions under which the dominant contribution to the excess risk comes from the decision boundary of the optimal Bayes classifier, but we also show that if these conditions are not satisfied, then the dominant contribution may arise from the tails of the marginal distribution of the features. Moreover, we prove that, provided the $d$-dimensional marginal distribution of the features has a finite $\rho $th moment for some $\rho >4$ (as well as other regularity conditions), a local choice of $k$ can yield a rate of convergence of the excess risk of $O(n^{-4/(d+4)})$, where $n$ is the sample size, whereas for the standard $k$-nearest neighbour classifier, our theory would require $d\geq 5$ and $\rho >4d/(d-4)$ finite moments to achieve this rate. These results motivate a new $k$-nearest neighbour classifier for semi-supervised learning problems, where the unlabelled data are used to obtain an estimate of the marginal feature density, and fewer neighbours are used for classification when this density estimate is small. Our worst-case rates are complemented by a minimax lower bound, which reveals that the local, semi-supervised $k$-nearest neighbour classifier attains the minimax optimal rate over our classes for the excess risk, up to a subpolynomial factor in $n$. These theoretical improvements over the standard $k$-nearest neighbour classifier are also illustrated through a simulation study.


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Timothy I. Cannings. Thomas B. Berrett. Richard J. Samworth. "Local nearest neighbour classification with applications to semi-supervised learning." Ann. Statist. 48 (3) 1789 - 1814, June 2020.


Received: 1 August 2018; Revised: 1 May 2019; Published: June 2020
First available in Project Euclid: 17 July 2020

zbMATH: 07241612
MathSciNet: MR4124344
Digital Object Identifier: 10.1214/19-AOS1868

Primary: 62G20

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.48 • No. 3 • June 2020
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