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Dimensionality Reduction

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Computer Vision

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Synonyms

Dimensional compression; Dimensional embedding; Dimension reduction

Related Concepts

Feature Selection

Definition

Dimensionality reduction is the process of reducing the dimension of the vector space spanned by feature vectors (pattern vectors). Various kinds of reduction can be achieved by defining a map from the original space into a dimensionality-reduced space.

Background

The feature space, i.e., the vector space spanned by feature vectors (pattern vectors) defined on d-dimensional space, can be transformed into a vector space of lower-dimension d′( < d) spanned by d′-dimensional feature vectors through linear or nonlinear transformation. This transformation allows feature vectors to be represented by lower-dimensional vectors, and various kinds of vector operations and statistical analysis, such as multivariate analysis, machine learning, clustering, and classification, become less expensive to perform. Moreover, it tackles the “curse of dimensionality,” the various...

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Author information

Authors and Affiliations

  1. NTT Communication Science Laboratories, 2-4 Hikaridai Seika-cho, 619-0237, Soraku-gun, Kyoto, Japan

    Eisaku Maeda

Authors
  1. Eisaku Maeda
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Corresponding author

Correspondence to Eisaku Maeda .

Editor information

Editors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Katsushi Ikeuchi

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© 2014 Springer Science+Business Media New York

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Maeda, E. (2014). Dimensionality Reduction. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_652

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