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Sparse on-line gaussian processes.

Lehel Csató, Manfred Opper

    Neural Computation
    |February 28, 2002
    PubMed
    Summary
    This summary is machine-generated.

    We present a novel sparse representation for Gaussian Process (GP) models, enabling efficient analysis of large datasets. This method enhances prediction accuracy and provides robust Bayesian error measures for complex machine learning tasks.

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    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • Bayesian Inference

    Background:

    • Gaussian Process (GP) models are powerful Bayesian kernel machines.
    • Traditional GP models face scalability challenges with large datasets.
    • Efficient computation and prediction are crucial for GP model applications.

    Purpose of the Study:

    • To develop a sparse representation approach for Gaussian Process models.
    • To overcome the limitations of standard GP models for large-scale data.
    • To enable efficient propagation of predictions and Bayesian error measures.

    Main Methods:

    • Combines a Bayesian on-line algorithm with sequential subsampling.
    • Utilizes parameterization and projection techniques in reproducing kernel Hilbert spaces.

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  • Derives recursions for effective parameters and a sparse Gaussian approximation of the posterior process.

    • Main Results:

    • Achieved sparse representations of Gaussian Process models.
    • Enabled efficient propagation of predictions and Bayesian error measures.
    • Demonstrated significance and robustness across various experiments.

    Conclusions:

    • The proposed approach effectively addresses the scalability issues of GP models.
    • This method offers a robust and efficient solution for large-data machine learning.
    • The technique facilitates accurate predictions and reliable uncertainty quantification.