An Empirical Evaluation of Intrinsic Dimension Estimators

  • Cristian Bustos
  • Gonzalo Navarro
  • Nora Reyes
  • Rodrigo ParedesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9371)


We study the practical behavior of different algorithms that aim to estimate the intrinsic dimension (ID) in metric spaces. Some of these algorithms were specifically developed to evaluate the complexity of searching in metric spaces, based on different theories related to the distribution of distances between objects on such spaces. Others were originally designed for vector spaces only, and have been extended to general metric spaces. To empirically evaluate the fitness of various ID estimations with the actual difficulty of searching in metric spaces, we compare one representative of each of the broadest families of metric indices: those based on pivots and those based on compact partitions. Our preliminary conclusions are that Fastmap and the measure called Intrinsic Dimensionality fit best their purpose.


Intrinsic Dimensionality Target Space Empirical Evaluation Range Query Search Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Cristian Bustos
    • 1
  • Gonzalo Navarro
    • 2
  • Nora Reyes
    • 1
  • Rodrigo Paredes
    • 3
    Email author
  1. 1.Departamento de InformáticaUniversidad Nacional de San LuisSan LuisArgentina
  2. 2.Department of Computer Science, Center of Biotechnology and BioengineeringUniversity of ChileSantiagoChile
  3. 3.Departamento de Ciencias de la ComputaciónUniversidad de TalcaCuricóChile

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