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

Author name not available (Why is that?)

Publication date: 6 December 2017

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.

Has companion code repository: https://github.com/lirongx/SubNN

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