Metric Embedding into the Hamming Space with the n-Simplex Projection

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Year of publication 2019
Type Article in Proceedings
Conference Similarity Search and Applications: 12th International Conference, SISAP 2019, Newark, New Jersey, USA, October 2-4, 2019, Proceedings
MU Faculty or unit

Faculty of Informatics

Keywords Similarity search; Space transformation; Hamming Embedding; n-Simplex Projection; sketch
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Description 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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