Encyclopedia of Biometrics

2015 Edition
| Editors: Stan Z. Li, Anil K. Jain

Manifold Learning

  • Philippos Mordohai
  • Gerard Medioni
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7488-4_301


Manifold learning is the process of estimating the structure of a manifold from a set of samples, also referred to as observations or instances, taken from the manifold. It is a subfield of machine learning that operates in continuous domains and learns from observations that are represented as points in a Euclidean space, referred to as the ambient space. This type of learning, to Mitchell, is termed instance-based or memory-based learning [1]. The goal of such learning is to discover the underlying relationships between observations, on the assumption that they lie in a limited part of the space, typically a manifold, the intrinsic dimensionality of a manifold of which is an indication of the degrees of freedom of the underlying system.


Manifold learning has attracted considerable attention of the machine learning community, due to a wide spectrum of applications in domains such as pattern recognition, data mining, biometrics, function approximation, and...

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Philippos Mordohai
    • 1
  • Gerard Medioni
    • 2
  1. 1.Stevens Institute of TechnologyPAUSA
  2. 2.University of Southern CaliforniaLos AngelesUSA