|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection|
Silva, Y. N.
|Citation:||Amato G, Chávez E, Connor R, Falchi F, Gennaro C & Vadicamo L (2018) Re-ranking Permutation-Based Candidate Sets with the n-Simplex Projection. In: Marchand-Maillet S, Silva YN & Chavez E (eds.) 11th International Conference on Similarity Search and Applications, SISAP 2018. Lecture Notes in Computer Science, 11223. SISAP 2018: International Conference on Similarity Search and Applications, Lima, Peru, 07.10.2018-09.10.2018. Cham, Switzerland: Springer Verlag, pp. 3-17. https://doi.org/10.1007/978-3-030-02224-2_1|
|Series/Report no.:||Lecture Notes in Computer Science, 11223|
|Conference Name:||SISAP 2018: International Conference on Similarity Search and Applications|
|Conference Dates:||2018-10-07 - 2018-10-09|
|Conference Location:||Lima, Peru|
|Abstract:||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.|
|Status:||AM - Accepted Manuscript|
|Rights:||This is a post-peer-review, pre-copyedit version of a paper published in Marchand-Maillet S, Silva YN & Chavez E (eds.) 11th International Conference on Similarity Search and Applications, SISAP 2018. Lecture Notes in Computer Science, 11223. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-02224-2_1|
|SISAP_2018_reranking.pdf||Fulltext - Accepted Version||1.41 MB||Adobe PDF||View/Open|
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