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Approximate Kernel Selection via Matrix Approximation.

Lizhong Ding, Shizhong Liao, Yong Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |January 17, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces approximate kernel selection methods, enhancing generalization for kernel methods. These novel algorithms leverage kernel matrix approximation for efficient and accurate kernel selection.

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    Area of Science:

    • Machine Learning
    • Computational Statistics

    Background:

    • Kernel selection is crucial for the performance of kernel methods.
    • Existing methods can be computationally intensive.

    Purpose of the Study:

    • To develop an approximate approach for kernel selection.
    • To establish theoretical foundations for approximate kernel selection.
    • To design efficient algorithms for kernel selection.

    Main Methods:

    • Defining and analyzing approximate consistency for kernel selection problems.
    • Utilizing Multilevel Circulant Matrix (MCM) approximation and Nyström approximation.
    • Developing selection criteria based on error estimation.

    Main Results:

    • Proving approximate consistency for MCM and Nyström approximations.
    • Establishing theoretical guarantees for approximate kernel selection.
    • Designing algorithms with linear or quasi-linear complexity.

    Conclusions:

    • The proposed approximate kernel selection methods are theoretically sound and effective.
    • These methods offer significant computational advantages over exact approaches.
    • The study provides a solid foundation for future research in approximate kernel selection.