Gaussian process classification for variable fidelity data

Nikita Klyuchnikov, Evgeny Burnaev

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by applying a co-kriging schema on latent functions, which allows the model to account item-dependent labeling discrepancy. We provide an extension of Laplace inference for Gaussian process classification, that takes into account multi-fidelity data. We evaluate the proposed method on real and synthetic datasets and show that it is more resistant to different levels of discrepancy between sources than other approaches for data fusion. Our method can provide accuracy/cost trade-off for a number of practical tasks such as crowd-sourced data annotation and feasibility regions construction in engineering design.

    Original languageEnglish
    Pages (from-to)345-355
    Number of pages11
    JournalNeurocomputing
    Volume397
    DOIs
    Publication statusPublished - 15 Jul 2020

    Keywords

    • Gaussian process classification
    • Laplace inference
    • Variable fidelity data

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