SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search

Citation

Vadicamo L, Connor R, Falchi F, Gennaro C & Rabitti F (2019) SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search. In: SISAP 2019: Similarity Search and Applications. Lecture Notes in Computer Science, 11807. SISAP2019: International Conference on Similarity Search and Applications, Newark, NJ, USA, 02.10.2019-04.10.2019. Cham, Switzerland: Springer, pp. 40-48. https://doi.org/10.1007/978-3-030-32047-8_4

Abstract
Many approaches for approximate metric search rely on a permutation-based representation of the original data objects. The main advantage of transforming metric objects into permutations is that the latter can be efficiently indexed and searched using data structures such as inverted-files and prefix trees. Typically, the permutation is obtained by ordering the identifiers of a set of pivots according to their distances to the object to be represented. In this paper, we present a novel approach to transform metric objects into permutations. It uses the object-pivot distances in combination with a metric transformation, called n-Simplex projection. The resulting permutation-based representation , named SPLX-Perm, is suitable only for the large class of metric space satisfying the n-point property. We tested the proposed approach on two benchmarks for similarity search. Our preliminary results are encouraging and open new perspectives for further investigations on the use of the n-Simplex projection for supporting permutation-based indexing.

Keywords
approximate metric search; permutation-based indexing; metric embedding; n-point property; n-Simplex projection