Learning to Prune in Metric and Non-Metric Spaces

Leonid Boytsov, Bilegsaikhan Naidan
2013 Neural Information Processing Systems  
Our focus is on approximate nearest neighbor retrieval in metric and non-metric spaces. We employ a VP-tree and explore two simple yet effective learning-toprune approaches: density estimation through sampling and "stretching" of the triangle inequality. Both methods are evaluated using data sets with metric (Euclidean) and non-metric (KL-divergence and Itakura-Saito) distance functions. Conditions on spaces where the VP-tree is applicable are discussed. The VP-tree with a learned pruner is
more » ... ared against the recently proposed state-of-the-art approaches: the bbtree, the multi-probe locality sensitive hashing (LSH), and permutation methods. Our method was competitive against state-of-the-art methods and, in most cases, was more efficient for the same rank approximation quality.
dblp:conf/nips/BoytsovN13 fatcat:lantcw4ovvawhh57o6mydziyhe