אריה קנטורוביץ

אקדמי בכיר

Near-Optimal Sample Compression for Nearest Neighbors

Lee Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch

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.

שפת פרסום אנגלית
דפים 4120-4128
כתב עת IEEE Transactions on Information Theory
כרך 64
נושא מספר 6
סטטוס פרסום פורסם - 01.06.2018

Keywords

Nearest neighbor methods

ASJC Scopus subject areas

Information Systems
Computer Science Applications
Library and Information Sciences
גישה למסמך
10.1109/TIT.2018.2822267
קבצים וקישורים אחרים
Link to publication in Scopus