Multiscale covariance fields, local scales, and shape transforms

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dc.contributor.authorMartinez, Diego H. Diazen
dc.contributor.authorMemoli, Facundoen
dc.contributor.authorMio, Washingtonen
dc.contributor.conferenceInternational SEE Conference on Geometric Science of Information (1st : 2013 : Paris)en
dc.contributor.schoolSchool of Computer Scienceen
dc.date.issued2013en
dc.description.abstractWe introduce the notion of multiscale covariance tensor fields associated with a probability measure on Euclidean space and use these fields to define local scales at a point and to construct shape transforms. Local scales at x may be interpreted as scales at which key geometric features of the data organization around x are revealed. Shape transforms are employed to identify points that are most salient in terms of the local-global shape of a probability distribution, yielding a compact summary of the geometry of the distribution.en
dc.description.statementofresponsibilityDiego H. Diaz Martinez, Facundo Mémoli, and Washington Mioen
dc.identifier.citationLecture Notes in Computer Science, 2013 / F. Nielsen, F. Barbaresco (eds.), pp.794-801en
dc.identifier.doi10.1007/978-3-642-40020-9_89en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/2440/83724
dc.language.isoenen
dc.publisherSpringeren
dc.rights© Springer-Verlag Berlin Heidelberg 2013en
dc.subjectcovariance fields; local scales; shape features; shape transformsen
dc.titleMultiscale covariance fields, local scales, and shape transformsen
dc.typeConference paperen

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