Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Curse of Dimensionality

  • Eamonn Keogh
  • Abdullah Mueen
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_192

Definition

The curse of dimensionality is a term introduced by Bellman to describe the problem caused  by the exponential increase in volume associated with adding extra dimensions to Euclidean space (Bellman,  1957).
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Recommended Reading

  1. The major database (SIGMOD, VLDB, PODS), data mining (SIGKDD, ICDM, SDM), and machine learning  (ICML, NIPS) conferences typically feature several papers which explicitly address the curse of dimensionality each year.Google Scholar
  2. Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (2001). On the surprising behavior of distance metrics in high dimensional spaces. In ICDT (pp. 420–434). London, England.Google Scholar
  3. Bellman, R. E. (1957). Dynamic programming. Princeton, NJ: Princeton University Press.MATHGoogle Scholar
  4. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008). Querying and mining of time series data: Experimental comparison of representations and distance measures. In Proceedings of the VLDB endowment (Vol. 1, pp. 1542–1552). Auckland, NewZealand.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Eamonn Keogh
  • Abdullah Mueen

There are no affiliations available