Learning to Prune in Metric and Non-Metric Spaces

By Leonid Boytsov, Bilegsaikhan Naidan. 2013

22 citations recorded by the source corpus.

Subfields: Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

1. Dmitry A. Yashunin
2. Yury Malkov

Citations in the corpus

• Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, methodology): “A general metric space algorithm with metric pruning [50] implemented as a part of nmslib 1.1.”
• Measuring Association on Topological Spaces Using Kernels and Geometric Graphs (2020, background): “Deb, Ghosal and Sen/Measuring Association on Topological Spaces 4 Note that the construction of k-NNGs can be carried out in rather general spaces provided there is some metric or a notion of “similarity” between two elements in that space (see [19, 6, 70, 58]).”
• Comparative Analysis of Data Structures for Approximate Nearest Neighbor Search (2014, background): “Whenever exact search methods for non-metric spaces do exist, they also seem to suffer from the curse of dimensionality [6][7].”
• Approximate Nearest Neighbor Search on High Dimensional Data - Experiments, Analyses, and Improvement (v1.0) (2016, methodology): “We evaluate FLANN(12) ([33], TPAMI’14), Annoy(13), and an advanced Vantage-Point tree [10] (VP-tree(10), NIPS’13).”