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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Vadicamo, Lucia
Connor, Richard
Falchi, Fabrizio
Gennaro, Claudio
Rabitti, Fausto
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Title: 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.
Issue Date: 2019
Series/Report no.: Lecture Notes in Computer Science, 11807
Conference Name: SISAP2019: International Conference on Similarity Search and Applications
Conference Dates: 2019-10-02 - 2019-10-04
Conference Location: Newark, NJ, USA
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.
Status: AM - Accepted Manuscript
Rights: This is a post-peer-review, pre-copyedit version of a paper published in Amato G., Gennaro C., Oria V., Radovanović M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science, vol 11807. Springer, Cham. The final authenticated version is available online at:

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