Homology and persistent homology

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dc.contributor.advisorBelchí Guillamón, Francisco
dc.contributor.authorNobbe Fisas, Fritz Pere
dc.date.accessioned2020-06-12T08:52:37Z
dc.date.available2020-06-12T08:52:37Z
dc.date.issued2020-01-19
dc.descriptionTreballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Francisco Belchí Guillamónca
dc.description.abstract[en] Extracting information from data sets that are high-dimensional, incomplete and noisy is generally challenging. The aim of this work is to explain a homology theory for data sets, called Persistent Homology, and the topology and algebra behind it. Moreover, we will show different ways to represent it and finally computing some examples with the help of the GUDHI software for Python.ca
dc.format.extent48 p.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2445/165324
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Fritz Pere Nobbe Fisas, 2020
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Matemàtiques
dc.subject.classificationTopologia algebraicaca
dc.subject.classificationTreballs de fi de grau
dc.subject.classificationHomologiaca
dc.subject.classificationAnàlisi multivariableca
dc.subject.classificationPython (Llenguatge de programació)ca
dc.subject.otherAlgebraic topologyen
dc.subject.otherBachelor's theses
dc.subject.otherHomologyen
dc.subject.otherMultivariate analysisen
dc.subject.otherPython (Computer program language)en
dc.titleHomology and persistent homologyca
dc.typeinfo:eu-repo/semantics/bachelorThesisca

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