Papers › Achieving the time of $1$-NN, but the accuracy of k-NN
Achieving the time of $1$-NN, but the accuracy of k-NN
Lirong Xue, Samory Kpotufe
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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