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

  • Lucia VadicamoEmail author
  • Vladimir Mic
  • Fabrizio Falchi
  • Pavel Zezula
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11807)


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.


Sketch Metric search Metric embedding n-point property 



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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lucia Vadicamo
    • 1
    Email author
  • Vladimir Mic
    • 2
  • Fabrizio Falchi
    • 1
  • Pavel Zezula
    • 2
  1. 1.Institute of Information Science and Technologies (ISTI)CNRPisaItaly
  2. 2.Masaryk UniversityBrnoCzech Republic

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