Predictive Active Set Selection Methods for Gaussian Processes

Ricardo Henao, Ole Winther

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists of a two step alternating procedure of active set update rules and hyperparameter optimization based upon marginal likelihood maximization. The active set update rules rely on the ability of the predictive distributions of a Gaussian process classifier to estimate the relative contribution of a datapoint when being either included or removed from the model. This means that we can use it to include points with potentially high impact to the classifier decision process while removing those that are less relevant. We introduce two active set rules based on different criteria, the first one prefers a model with interpretable active set parameters whereas the second puts computational complexity first, thus a model with active set parameters that directly control its complexity. We also provide both theoretical and empirical support for our active set selection strategy being a good approximation of a full Gaussian process classifier. Our extensive experiments show that our approach can compete with state-of-the-art classification techniques with reasonable time complexity. Source code publicly available at http://cogsys.imm.dtu.dk/passgp.
    Original languageEnglish
    JournalNeurocomputing
    Volume80
    Issue number15
    ISSN0925-2312
    DOIs
    Publication statusPublished - 2012

    Keywords

    • Gaussian process classification
    • Active set selection
    • Predictive distribution
    • Expectation propagation

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