Near-Optimal Sample Compression for Nearest Neighbors

Lee Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our performance bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds. Some encouraging empirical results are also presented.

Original languageEnglish
Pages (from-to)4120-4128
Number of pages9
JournalIEEE Transactions on Information Theory
Issue number6
StatePublished - Jun 2018


  • Nearest neighbor methods


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