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Learning to Prune in Metric and Non-Metric Spaces
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 isdblp:conf/nips/BoytsovN13 fatcat:lantcw4ovvawhh57o6mydziyhe