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On using prototype reduction schemes and classifier fusion strategies to optimize kernel-based nonlinear subspace

Sang-Woon Kim, B J Oommen

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 8, 2005
    PubMed
    Summary
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    Kernel-based Nonlinear Subspace (KNS) methods face computational challenges with large datasets. This study introduces a Prototype Reduction Scheme (PRS) and Classifier Fusion Strategy (CFS) to significantly reduce computation time without impacting accuracy.

    Area of Science:

    • Machine Learning
    • Computational Statistics
    • Pattern Recognition

    Background:

    • Kernel-based Nonlinear Subspace (KNS) methods rely on kernel matrices, posing computational challenges for large datasets.
    • The dimensionality of kernel matrices grows with the number of data points, hindering scalability.
    • Efficient computation of projections in feature space is crucial for KNS performance.

    Purpose of the Study:

    • To address the computational burden of KNS methods on large datasets.
    • To develop a scalable approach for Kernel-based Nonlinear Subspace analysis.
    • To maintain classification accuracy while improving computational efficiency.

    Main Methods:

    • Data is subdivided into smaller subsets.
    • A Prototype Reduction Scheme (PRS) is employed as a preprocessing module to generate refined prototypes.

    Related Experiment Videos

  • A Classifier Fusion Strategy (CFS) is used as a postprocessing module to combine results from individual KNS classifications.

    • Main Results:

    • The proposed mechanism significantly reduces prototype extraction time.
    • Computation time is substantially decreased.
    • Classification accuracy is preserved.
    • Significant computational advantages are demonstrated for large datasets, particularly within a parallel processing framework.

    Conclusions:

    • The integration of PRS and CFS offers an efficient solution for large-scale KNS applications.
    • The method provides a favorable trade-off between computational cost and classification performance.
    • This approach enhances the practicality of KNS methods for big data scenarios.