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We present a simple algorithm for identifying and correcting real-valuednoisy labels from a mixture of clean and corrupted sample points using Gaussianprocess regression. A heteroscedastic noise model is employed, in whichadditive Gaussian noise terms with independent variances are associated witheach and all of the observed labels. Optimizing the noise model using maximumlikelihood estimation leads to the containment of the GPR model's predictiveerror by the posterior standard deviation in leave-one-out cross-validation. Amultiplicative update scheme is proposed for solving the maximum likelihoodestimation problem under non-negative constraints. While we provide proof ofconvergence for certain special cases, the multiplicative scheme hasempirically demonstrated monotonic convergence behavior in virtually all ournumerical experiments. We show that the presented method can pinpoint corruptedsample points and lead to better regression models when trained on syntheticand real-world scientific data sets.

Detecting Label Noise via Leave-One-Out Cross-Validation