Abstract
Transformations of data objects into the Hamming space are often exploited to speed-up the similarity search in metric spaces. Techniques applicable in generic metric spaces require expensive learning, e.g., selection of pivoting objects. However, when searching in common Euclidean space, the best performance is usually achieved by transformations specifically designed for this space. We propose a novel transformation technique that provides a good trade-off between the applicability and the quality of the space approximation. It uses the n-Simplex projection to transform metric objects into a low-dimensional Euclidean space, and then transform this space to the Hamming space. We compare our approach theoretically and experimentally with several techniques of the metric embedding into the Hamming space. We focus on the applicability, learning cost, and the quality of search space approximation.
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Notes
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The proof is made trivially by a selection of all objects from the data set \(S\) as pivots.
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Acknowledgements
The work was partially supported by VISECH ARCO-CNR, CUP B56J17001330004, and AI4EU project, funded by the EC (H2020 - Contract n. 825619). This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/ 16_019/0000822).
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Vadicamo, L., Mic, V., Falchi, F., Zezula, P. (2019). Metric Embedding into the Hamming Space with the n-Simplex Projection. 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_23
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