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LESS: a model-based classifier for sparse subspaces.

Cor J Veenman1, David M J Tax

  • 1Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands. C.J.Veenman@ewi.tudelft.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 22, 2005
PubMed
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The LESS classifier addresses high-dimensional data challenges by finding linear discriminants in sparse subspaces. It offers competitive performance with fewer dimensions, outperforming other methods on several datasets.

Area of Science:

  • Machine Learning
  • Data Science
  • Pattern Recognition

Background:

  • High-dimensional data presents challenges for classifier design, particularly with small sample sizes.
  • Key issues include finding generalizable separating hyperplanes and identifying relevant features.
  • Existing methods often struggle with the curse of dimensionality.

Purpose of the Study:

  • To propose the Lowest Error in a Sparse Subspace (LESS) classifier for high-dimensional data.
  • To efficiently find linear discriminants within a sparse subspace.
  • To balance subspace sparseness and classification accuracy via a regularization parameter.

Main Methods:

  • The LESS classifier incorporates a simple data model.
  • It utilizes a regularization parameter to control the trade-off between sparseness and accuracy.

Related Experiment Videos

  • Linear discriminants are efficiently found in a sparse subspace.

    • Main Results:

    • LESS demonstrates competitive performance on various high-dimensional datasets.
    • It achieves comparable accuracy to state-of-the-art classifiers like LASSO and Support Vector Machines.
    • LESS effectively reduces the number of dimensions required for classification.

    Conclusions:

    • The LESS classifier is an effective tool for handling high-dimensional, small-sample-size problems.
    • It offers a robust approach to feature selection and classification.
    • LESS provides a favorable balance between model complexity and predictive performance.