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Dimensionality Reduction

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Computer Vision

Synonyms

Dimension reduction; Dimensional compression; Dimensional embedding

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. Tokyo Denki University, Tokyo, Japan

    Eisaku Maeda

Authors
  1. Eisaku Maeda
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Corresponding author

Correspondence to Eisaku Maeda .

Section Editor information

  1. Tohoku University, Sendai, Japan

    Koichiro Deguchi

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Maeda, E. (2020). Dimensionality Reduction. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_652-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_652-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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