dc.creator | Atienza Martínez, María Nieves | es |
dc.creator | González Díaz, Rocío | es |
dc.creator | Rucco, Matteo | es |
dc.date.accessioned | 2019-07-01T10:59:32Z | |
dc.date.available | 2019-07-01T10:59:32Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Atienza Martínez, M.N., González Díaz, R. y Rucco, M. (2016). Separating Topological Noise from Features Using Persistent Entropy. En STAF 2016: Collocated Workshops: DataMod, GCM, HOFM, MELO, SEMS, VeryComp (3-12), Vienna Austria: Springer. | |
dc.identifier.isbn | 978-3-319-50229-8 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/87712 | |
dc.description.abstract | Topology is the branch of mathematics that studies shapes
and maps among them. From the algebraic definition of topology a new
set of algorithms have been derived. These algorithms are identified
with “computational topology” or often pointed out as Topological Data
Analysis (TDA) and are used for investigating high-dimensional data in a
quantitative manner. Persistent homology appears as a fundamental tool
in Topological Data Analysis. It studies the evolution of k−dimensional
holes along a sequence of simplicial complexes (i.e. a filtration). The set
of intervals representing birth and death times of k−dimensional holes
along such sequence is called the persistence barcode. k−dimensional
holes with short lifetimes are informally considered to be topological
noise, and those with a long lifetime are considered to be topological
feature associated to the given data (i.e. the filtration). In this paper, we
derive a simple method for separating topological noise from topological
features using a novel measure for comparing persistence barcodes called
persistent entropy. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad MTM2015-67072-P | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | STAF 2016: Collocated Workshops: DataMod, GCM, HOFM, MELO, SEMS, VeryComp (2016), p 3-12 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Persistent homology | es |
dc.subject | Persistence barcodes | es |
dc.subject | Shannon entropy | es |
dc.subject | Topological noise | es |
dc.subject | Topological feature | es |
dc.title | Separating Topological Noise from Features Using Persistent Entropy | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.relation.projectID | MTM2015-67072-P | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-50230-4_1 | es |
dc.identifier.doi | 10.1007/978-3-319-50230-4_1 | es |
idus.format.extent | 10 | es |
dc.publication.initialPage | 3 | es |
dc.publication.endPage | 12 | es |
dc.eventtitle | STAF 2016: Collocated Workshops: DataMod, GCM, HOFM, MELO, SEMS, VeryComp | es |
dc.eventinstitution | Vienna Austria | es |
dc.relation.publicationplace | Berlin | es |