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Deep Neural Networks for Image-Based Dietary Assessment
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Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent.

En-Liang Hu, James T Kwok

    IEEE Transactions on Neural Networks and Learning Systems
    |October 25, 2014
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    Summary
    This summary is machine-generated.

    We introduce a scalable method for nonparametric kernel learning (NPKL) by combining low-rank approximation and block coordinate descent (BCD). This approach enhances computational efficiency and performance over existing NPKL solvers.

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    Area of Science:

    • Machine Learning
    • Optimization

    Background:

    • Nonparametric kernel learning (NPKL) offers flexibility by learning kernel matrices without predefined forms.
    • Traditional NPKL formulations as semidefinite programs (SDPs) face scalability challenges.

    Purpose of the Study:

    • To develop a scalable and efficient algorithm for NPKL.
    • To overcome the computational limitations of SDP-based NPKL.

    Main Methods:

    • Proposed a novel approach combining low-rank approximation with block coordinate descent (BCD).
    • Replaced the kernel matrix with a low-rank decomposition (V^T V) to bypass positive semidefinite constraints.
    • Applied BCD to optimize the low-rank matrix V column by column.

    Main Results:

    • The proposed algorithm demonstrates favorable convergence properties.
    • Achieved low computational complexity compared to existing methods.
    • Outperformed state-of-the-art NPKL solvers on various real-world datasets.

    Conclusions:

    • The combination of low-rank approximation and BCD provides an efficient and scalable solution for NPKL.
    • This method offers a practical alternative for learning kernel matrices in machine learning applications.