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On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers.

Sang-Woon Kim1, B John Oommen

  • 1Department of Computer Science and Engineering, Myongji University, Yongin, 449-728 Korea. kimsw@mju.ac.kr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 5, 2005
PubMed
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Selecting optimal subspace dimensions is crucial for Kernel-based Nonlinear Subspace (KNS) classifier performance. This study introduces a novel Overlapping criterion heuristic for efficient dimension selection, maintaining high classification accuracy.

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Computer Vision

Background:

  • Subspace dimensions significantly impact Kernel-based Nonlinear Subspace (KNS) classifier performance.
  • Current methods for dimension selection are often ad hoc, relying on cumulative proportions from kernel matrices.
  • Suboptimal dimension selection can lead to performance degradation due to subspace overlap or poor approximation.

Purpose of the Study:

  • To propose a systematic and efficient method for selecting optimal subspace dimensions in KNS classifiers.
  • To introduce a novel heuristic function, the Overlapping criterion, for guiding dimension selection.
  • To demonstrate the effectiveness of the proposed method in improving KNS classifier performance.

Main Methods:

  • Development of a search strategy coupled with the Overlapping criterion heuristic.

Related Experiment Videos

  • Application of the Overlapping criterion to prune the search space for optimal subspace dimensions.
  • Systematic evaluation of the proposed dimension selection mechanism.

    • Main Results:

    • The proposed method efficiently selects optimal or near-optimal subspace dimensions for KNS classifiers.
    • Experimental results show that the new mechanism achieves efficient dimension selection without compromising classification accuracy.
    • The Overlapping criterion effectively guides the search for optimal subspace dimensions.

    Conclusions:

    • The Overlapping criterion provides an efficient and effective approach for subspace dimension selection in KNS methods.
    • This systematic approach overcomes the limitations of ad hoc dimension selection techniques.
    • The proposed method enhances KNS classifier performance by optimizing subspace dimensionality.