Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)


In the realm of metric search, the permutation-based approaches have shown very good performance in indexing and supporting approximate search on large databases. These methods embed the metric objects into a permutation space where candidate results to a given query can be efficiently identified. Typically, to achieve high effectiveness, the permutation-based result set is refined by directly comparing each candidate object to the query one. Therefore, one drawback of these approaches is that the original dataset needs to be stored and then accessed during the refining step. We propose a refining approach based on a metric embedding, called n-Simplex projection, that can be used on metric spaces meeting the n-point property. The n-Simplex projection provides upper- and lower-bounds of the actual distance, derived using the distances between the data objects and a finite set of pivots. We propose to reuse the distances computed for building the data permutations to derive these bounds and we show how to use them to improve the permutation-based results. Our approach is particularly advantageous for all the cases in which the traditional refining step is too costly, e.g. very large dataset or very expensive metric function.


Metric search Permutation-based indexing n-point property n-Simplex projection Metric embedding Distance bounds 



The work was partially funded by Smart News, “Social sensing for breaking news”, CUP CIPE D58C15000270008, and by VISECH, ARCO-CNR, CUP B56J17001330004.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Information Science and Technologies (ISTI), CNRPisaItaly
  2. 2.Department of Computer ScienceCICESEEnsenadaMexico
  3. 3.Department of Computing ScienceUniversity of StirlingStirlingScotland

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