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Twin Support Vector Machines for pattern classification.

Jayadeva1, R Khemchandani, Suresh Chandra

  • 1Department of Electrical Engineering, Indian Institute of Technology, Hauz-Khas, New Delhi, India. jayadeva@ee.iitd.ac.in

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 2, 2007
PubMed
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Twin Support Vector Machines (SVM) offer a faster binary classification method by solving two smaller SVM problems. This approach achieves good generalization and aids in discovering 2D data projections.

Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Support Vector Machines (SVM) are widely used for binary classification.
  • Conventional SVMs involve solving a single large optimization problem.

Purpose of the Study:

  • To introduce Twin SVM, a novel binary classifier.
  • To reduce computational complexity compared to traditional SVMs.
  • To explore its utility in dimensionality reduction.

Main Methods:

  • Proposing Twin SVM, which determines two nonparallel classification planes.
  • Solving two smaller, related SVM-type problems.
  • Utilizing a formulation in the spirit of proximal SVMs via generalized eigenvalues.

Main Results:

  • Twin SVM demonstrates faster performance on benchmark datasets.

Related Experiment Videos

  • The method achieves good generalization capabilities.
  • Effectively used for automatic discovery of two-dimensional data projections.

    • Conclusions:

    • Twin SVM provides an efficient alternative to conventional SVMs for binary classification.
    • The approach offers benefits in both speed and generalization.
    • Applicable for feature extraction and dimensionality reduction tasks.