Near-optimal sample compression for nearest neighbors Conference Paper uri icon


  • Abstract 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 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.

publication date

  • January 1, 2014