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Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent

dc.contributor.authorBoria, Nicolas
HAL ID: 21013
ORCID: 0000-0002-0548-4257
dc.contributor.authorNegrevergne, Benjamin
HAL ID: 172154
ORCID: 0000-0002-7074-8167
dc.contributor.authorYger, Florian
HAL ID: 17768
ORCID: 0000-0002-7182-8062
dc.date.accessioned2020-11-04T13:36:03Z
dc.date.available2020-11-04T13:36:03Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/21185
dc.language.isoenen
dc.subjectGraph space
dc.subject.ddc005en
dc.titleFréchet Mean Computation in Graph Space through Projected Block Gradient Descent
dc.typeCommunication / Conférence
dc.description.abstractenA fundamental concept in statistics is the concept of Fréchet sample mean. While its computation is a simple task in Euclidian space, the same does not hold for less structured spaces such as the space of graphs, where concepts of distance or mid-point can be hard to compute. We present some work in progress regarding new distance measures and new algorithms to compute the Fréchet mean in the space of Graphs.
dc.identifier.urlsitehttps://hal-normandie-univ.archives-ouvertes.fr/hal-02895832
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.conftitle28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020)
dc.relation.confdate2020-10
dc.relation.confcityBruges
dc.relation.confcountryFRANCE
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2021-01-12T14:41:17Z


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