Gaussian Process Classification for Variable Fidelity Data

TL;DR

This paper introduces a Gaussian process classification method that effectively combines variable fidelity data sources, improving robustness to discrepancies and enabling cost-effective, accurate classification in practical applications.

Contribution

It extends Laplace inference for Gaussian process classification to handle multi-fidelity data, offering a novel approach for data fusion with discrepancy modeling.

Findings

More resistant to source discrepancies than existing methods

Effective on both real and synthetic datasets

Enables accuracy/cost trade-offs in practical tasks

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.