Learning with Wasserstein barycenters and applications

26 Dec 2019G. DomazakisD. DrivaliarisS. KoukoulasG. PapayiannisA. TsekrekosA. Yannacopoulos

In this work, learning schemes for measure-valued data are proposed, i.e. data that their structure can be more efficiently represented as probability measures instead of points on $\R^d$, employing the concept of probability barycenters as defined with respect to the Wasserstein metric. Such type of learning approaches are highly appreciated in many fields where the observational/experimental error is significant (e.g. astronomy, biology, remote sensing, etc.).. (read more)

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