VADICAMO, Lucia, Vladimír MÍČ, Falchi FABRIZIO and Pavel ZEZULA. Metric Embedding into the Hamming Space with the n-Simplex Projection. In Similarity Search and Applications: 12th International Conference, SISAP 2019, Newark, New Jersey, USA, October 2-4, 2019, Proceedings. Cham: Springer International Publishing, 2019, p. 265-272. ISBN 978-3-030-32046-1. |
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@inproceedings{36486, author = {Vadicamo, Lucia and Míč, Vladimír and Fabrizio, Falchi and Zezula, Pavel}, address = {Cham}, booktitle = {Similarity Search and Applications: 12th International Conference, SISAP 2019, Newark, New Jersey, USA, October 2-4, 2019, Proceedings}, keywords = {Similarity search; Space transformation; Hamming Embedding; n-Simplex Projection; sketch}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Cham}, isbn = {978-3-030-32046-1}, pages = {265-272}, publisher = {Springer International Publishing}, title = {Metric Embedding into the Hamming Space with the n-Simplex Projection}, year = {2019} }
TY - JOUR ID - 36486 AU - Vadicamo, Lucia - Míč, Vladimír - Fabrizio, Falchi - Zezula, Pavel PY - 2019 TI - Metric Embedding into the Hamming Space with the n-Simplex Projection PB - Springer International Publishing CY - Cham SN - 9783030320461 KW - Similarity search KW - Space transformation KW - Hamming Embedding KW - n-Simplex Projection KW - sketch N2 - 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. ER -
VADICAMO, Lucia, Vladimír MÍČ, Falchi FABRIZIO and Pavel ZEZULA. Metric Embedding into the Hamming Space with the n-Simplex Projection. In \textit{Similarity Search and Applications: 12th International Conference, SISAP 2019, Newark, New Jersey, USA, October 2-4, 2019, Proceedings}. Cham: Springer International Publishing, 2019, p.~265-272. ISBN~978-3-030-32046-1.
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