Declutter and resample: Towards parameter free denoising

Mickaël Buchet, Tamal K. Dey, Jiayuan Wang & Yusu Wang
In many data analysis applications the following scenario is commonplace: we are given a point set that is supposed to sample a hidden ground truth $K$ in a metric space, but it got corrupted with noise so that some of the data points lie far away from $K$ creating outliers also termed as ambient noise. One of the main goals of denoising algorithms is to eliminate such noise so that the curated data lie within...
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