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.
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Acknowledgements
This work was partially supported by VISECH ARCO-CNR, CUP B56J17001330004, the AI4EU project, funded by the EC (H2020 - Contract n. 825619), and the Short-Term-Mobility (STM) program of the CNR.
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Vadicamo, L., Connor, R., Falchi, F., Gennaro, C., Rabitti, F. (2019). SPLX-Perm: A Novel Permutation-Based Representation for Approximate Metric Search. 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. https://doi.org/10.1007/978-3-030-32047-8_4
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