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Expectation-Maximization Algorithm

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Computer Vision

Synonyms

EM-algorithm

Related Concepts

Definition

The expectation-maximization algorithm iteratively maximizes the likelihood of a training sample with respect to unknown parameters of a probability model under the condition of missing information. The training sample is assumed to represent a set of independent realizations of a random variable defined on the underlying probability space.

Background

One of the main paradigms of statistical pattern recognition and Bayesian inference is to model the relation between the observable features \(x\in \mathcal {X}\) of an object and its hidden state \(y\in \mathcal {Y}\) by a joint probability measure p(x, y). This probability measure is, however, often known only up to some parameters θ ∈ Θ. It is thus necessary to estimate these parameters from a training sample, which is assumed to represent a sequence of independent realizations of a random variable. If, ideally,...

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University Prague, Prague, Czechia

    Boris Flach

  2. Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University Prague, Prague, Czechia

    Vaclav Hlavac

Authors
  1. Boris Flach
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  2. Vaclav Hlavac
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Corresponding author

Correspondence to Boris Flach .

Section Editor information

  1. Okayama University, Okayama, Japan

    Kenichi Kanatani

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Flach, B., Hlavac, V. (2020). Expectation-Maximization Algorithm. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_692-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_692-1

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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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