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Comments on: A unified algorithm for principal and minor components extraction.

Fa Long Luo1, Rolf Unbehauen

  • 1Lehrstuhl für Allgemeine und Theoretische Elektrotechnik, Universität Erlangen-Nürnberg, Cauerstrabetae 7, 91058, Erlangen, Germany

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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Chen et al.'s unified algorithm is a direct generalization of the invariant-norm algorithm. However, it is computationally intensive, and a more practical generalization is now available.

Area of Science:

  • Machine Learning
  • Neural Networks
  • Algorithm Analysis

Background:

  • The invariant-norm algorithm offers a robust approach to neural network learning.
  • A unified algorithm by Chen et al. was proposed as a generalization.

Purpose of the Study:

  • To analyze the unified algorithm proposed by Chen et al.
  • To highlight the computational limitations of this generalized algorithm.
  • To introduce a more practical and effective generalization.

Main Methods:

  • Comparative analysis of algorithms.
  • Evaluation of computational complexity in learning algorithms.

Main Results:

  • The unified algorithm is a direct generalization of the invariant-norm algorithm.

Related Experiment Videos

  • The direct-generalized unified algorithm is computationally intensive and impractical for learning.
  • A more effective and practical generalization has been developed.

    • Conclusions:

    • The direct generalization of the invariant-norm algorithm by Chen et al. is not suitable for practical neural network learning due to high computational cost.
    • A novel, more efficient generalization offers improved learning performance.