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dc.contributor.advisorBelchí Guillamón, Francisco-
dc.contributor.authorNobbe Fisas, Fritz Pere-
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
dc.format.extent48 p.-
dc.rightscc-by-nc-nd (c) Fritz Pere Nobbe Fisas, 2020-
dc.subject.classificationTopologia algebraicaca
dc.subject.classificationTreballs de fi de grau-
dc.subject.classificationAnàlisi multivariableca
dc.subject.classificationPython (Llenguatge de programació)ca
dc.subject.otherAlgebraic topologyen
dc.subject.otherBachelor's thesis-
dc.subject.otherMultivariate analysien
dc.subject.otherPython (Computer program language)en
dc.titleHomology and persistent homologyca
Appears in Collections:Treballs Finals de Grau (TFG) - Matemàtiques

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