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Discriminative nonlinear dimensionality reduction for improved classification

M Dolson1

  • 1Center for Research in Language, University of California, San Diego, USA.

International Journal of Neural Systems
|December 1, 1994
PubMed
Summary
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This study compares direct and indirect Multi-Layer Perception (MLP) classification. Integrating nonlinear dimensionality reduction into the indirect approach enhances MLP networks for improved temporal trajectory classification.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Multi-Layer Perception (MLP) networks are widely used for classification.
  • Traditional MLP training directly outputs class labels.
  • An indirect MLP approach trains a unique network per class, with mixed results.

Purpose of the Study:

  • To fundamentally compare direct and indirect MLP classification approaches.
  • To integrate nonlinear dimensionality reduction into the indirect MLP method.
  • To develop a novel MLP framework combining both approaches.

Main Methods:

  • Incorporating nonlinear dimensionality reduction into indirect MLP classification.
  • Developing a novel MLP framework integrating direct and indirect methods.

Related Experiment Videos

  • Generalizing new MLP networks to Learning Vector Quantization (LVQ) and subspace methods.

    • Main Results:

    • The integrated MLP framework offers a novel perspective on classification.
    • New MLP networks generalize existing methods like LVQ.
    • Application to temporal trajectory classification shows substantial performance improvements.

    Conclusions:

    • The proposed MLP framework enhances classification performance, particularly for temporal data.
    • Integrating advanced dimensionality reduction techniques offers a promising direction for MLP networks.
    • This work provides a unified view of MLP, LVQ, and subspace methods.