Robust Regression on Image Manifolds for Ordered Label Denoising
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Abstract
In this paper, we present a computationally effificient and non-parametric method for robust regression on manifolds. We apply our algorithm to the problem of correcting mislabeled examples from image collections with ordered (e.g., real-valued, ordinal) labels. Compared to related methods for robust regression, our method achieves superior denoising accuracy on a variety of data sets, with label corruption levels as high as 80%. For a diverse set of widely-used, large-scale, publicly-available data sets, our approach results in image labels that more accurately describe the associated images.
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