Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Dimension Reduction

  • Kasper Green Larsen
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_60-1


Dimensionality Reduction is a technique for taking a high-dimensional data set (data objects with many features/attributes) and replacing it with a much lower-dimensional data set while still preserving similarities between data objects. Dimensionality reduction is useful for reducing the memory requirements for storing a data set, as well as for speeding up algorithms.


In modern algorithm design and data analysis, we often face very high-dimensional data. High-dimensional data comes in many forms. As examples, we can think of a 10-megapixel image as a point with ten million coordinates, one for each pixel. In this way, an image becomes a ten million-dimensional point. Another often encountered example arises when we wish to compare documents based on similarity. One way to do this is to simply count how many times each dictionary word occurs in a document and compare documents based on these counts. This yields a representation of a document as a point with one...

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Aarhus UniversityAarhusDenmark

Section editors and affiliations

  • Paolo Ferragina
    • 1
  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly