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handle: 2445/165324
[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.
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Francisco Belchí Guillamón
Bachelor's thesis, Multivariate analysis, Multivariate analysi, Bachelor's theses, Homologia, Anàlisi multivariable, Python (Llenguatge de programació), Treballs de fi de grau, Topologia algebraica, Algebraic topology, Python (Computer program language), Homology
Bachelor's thesis, Multivariate analysis, Multivariate analysi, Bachelor's theses, Homologia, Anàlisi multivariable, Python (Llenguatge de programació), Treballs de fi de grau, Topologia algebraica, Algebraic topology, Python (Computer program language), Homology
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