Measuring the Difficulty of Distance-Based Indexing

  • Matthew Skala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3772)


Data structures for similarity search are commonly evaluated on data in vector spaces, but distance-based data structures are also applicable to non-vector spaces with no natural concept of dimensionality. The intrinsic dimensionality statistic of Chávez and Navarro provides a way to compare the performance of similarity indexing and search algorithms across different spaces, and predict the performance of index data structures on non-vector spaces by relating them to equivalent vector spaces. We characterise its asymptotic behaviour, and give experimental results to calibrate these comparisons.


Query Point Real Random Variate Intrinsic Dimensionality Extreme Order Statistic Uniform Vector 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Matthew Skala
    • 1
  1. 1.University of WaterlooWaterlooCanada

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