Achieving the time of $1$-NN, but the accuracy of $k$-NN

Lirong Xue; Samory Kpotufe

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arXiv · 1712.02369

Achieving the time of $1$-NN, but the accuracy of $k$-NN

Lirong Xue·Samory Kpotufe

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Abstract

We propose a simple approach which, given distributed computing resources, can nearly achieve the accuracy of $k$-NN prediction, while matching (or improving) the faster prediction time of $1$-NN. The approach consists of aggregating denoised $1$-NN predictors over a small number of distributed subsamples. We show, both theoretically and experimentally, that small subsample sizes suffice to attain similar performance as $k$-NN, without sacrificing the computational efficiency of $1$-NN.

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BibTeX RIS

Lirong Xue, Samory Kpotufe. 2017-12-06. Achieving the time of $1$-NN, but the accuracy of $k$-NN. https://arxiv.org/abs/1712.02369

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