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Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection: 11th International Conference, SISAP 2018, Lima, Peru, October 7–9, 2018, Proceedings

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... In the past, we developed and proposed various techniques to support approximate similarity research in metric spaces, including approaches that rely on transforming the data objects into permutations (Permutation-based indexing) [39,40,41,42], low-dimensional Euclidean vectors (Supermetric search) [43,44], or compact binary codes (Sketching technique) [45]. Moreover, for a class of metric space that satisfy the so called "4-point property " [46] we derived a new pruning rule named Hilbert Exclusion [47], which can be used with any indexing mechanism based on hyperplane partitioning in order to determine subset of data that do not need to be exhaustively inspected. ...
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