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Related Experiment Videos

Nonlinear fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares

Steve A Billings1, Kian L Lee

  • 1Department of Automatic Control and Systems Engineering, University of Sheffield, UK. s.billings@sheffield.ac.uk

Neural Networks : the Official Journal of the International Neural Network Society
|May 23, 2002
PubMed
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A new classification technique, based on the nonlinear Fisher discriminant (NFD) and minimum squared error, offers a parsimonious approach. This method, utilizing the orthogonal least squares (OLS) algorithm, shows competitive performance against state-of-the-art techniques.

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Statistical Classification

Background:

  • The nonlinear Fisher discriminant (NFD) is a key concept in statistical pattern recognition.
  • Minimum squared error cost functions are widely used in classification tasks.
  • Finding parsimonious models for nonlinear discriminant functions is computationally challenging.

Purpose of the Study:

  • To establish a direct relationship between minimum squared error cost functions and the nonlinear Fisher discriminant (NFD).
  • To introduce a novel classification technique based on this relationship.
  • To evaluate the performance of the proposed technique against existing methods.

Main Methods:

  • Derivation of the nonlinear discriminant function using a minimum squared error cost function.

Related Experiment Videos

  • Application of the orthogonal least squares (OLS) algorithm for model simplification.
  • Testing two classification techniques on diverse real and artificial datasets.

    • Main Results:

    • The minimum squared error cost function is shown to be directly related to the NFD.
    • The orthogonal least squares (OLS) algorithm successfully identifies parsimonious nonlinear discriminant functions.
    • The proposed classification techniques demonstrate favorable performance compared to state-of-the-art methods on various datasets.

    Conclusions:

    • A novel and efficient classification method is presented, leveraging the NFD and OLS.
    • The technique offers a parsimonious and effective approach to nonlinear classification problems.
    • The findings suggest potential for improved performance in complex classification tasks.