Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Curse of Dimensionality

  • Lei ChenEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_133


Dimensionality curse


The curse of dimensionality, first introduced by Bellman [1], indicates that the number of samples needed to estimate an arbitrary function with a given level of accuracy grows exponentially with respect to the number of input variables (i.e., dimensionality) of the function.

For similarity search (e.g., nearest neighbor query or range query), the curse of dimensionality means that the number of objects in the data set that need to be accessed grows exponentially with the underlying dimensionality.

Key Points

The curse of dimensionality is an obstacle for solving dynamic optimization problems by backwards induction. Moreover, it renders machine learning problems complicated, when it is necessary to learn a state-of-nature from finite number data samples in a high dimensional feature space. Finally, the curse of dimensionalityseriously affects the query performance for similarity search over multidimensional indexes because, in high dimensions,...

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Recommended Reading

  1. 1.
    Bellman RE. Adaptive control processes. Princeton: Princeton University Press; 1961.zbMATHCrossRefGoogle Scholar
  2. 2.
    Beyer KS, Goldstein J, Ramakrishnan R, Shaft U. When is “Nearest Neighbor” meaningful? In: Proceedings of the 7th International Conference on Database Theory; 1999. p. 217–35.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hong Kong University of Science and TechnologyHong KongChina

Section editors and affiliations

  • Dimitris Papadias
    • 1
  1. 1.Dept. of Computer Science and Eng.Hong Kong Univ. of Science and TechnologyKowloonHong Kong SAR