dc.creator | Rucco, Matteo | es |
dc.creator | González Díaz, Rocío | es |
dc.creator | Jiménez Rodríguez, María José | es |
dc.creator | Atienza Martínez, María Nieves | es |
dc.creator | Cristalli, Cristina | es |
dc.creator | Concettoni, Enrico | es |
dc.creator | Ferrante, Andrea | es |
dc.creator | Merelli, Emanuela | es |
dc.date.accessioned | 2019-07-01T08:23:56Z | |
dc.date.available | 2019-07-01T08:23:56Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Rucco, M., González Díaz, R., Jiménez Rodríguez, M.J., Atienza Martínez, M.N., Cristalli, C., Concettoni, E.,...,Merelli, E. (2017). A new topological entropy-based approach for measuring similarities among piecewise linear functions. Signal Processing, 134 (may 2017), 130-138. | |
dc.identifier.issn | 0165-1684 | es |
dc.identifier.uri | https://hdl.handle.net/11441/87688 | |
dc.description.abstract | In this paper we present a novel methodology based on a topological entropy,
the so-called persistent entropy, for addressing the comparison between discrete
piecewise linear functions. The comparison is certi ed by the stability
theorem for persistent entropy. The theorem is used in the implementation
of a new algorithm. The algorithm transforms a discrete piecewise linear
function into a ltered simplicial complex that is analyzed with persistent
homology and persistent entropy. Persistent entropy is used as discriminant
feature for solving the supervised classi cation problem of real long
length noisy signals of DC electrical motors. The quality of classi cation
is stated in terms of the area under receiver operating characteristic curve
(AUC=94.52%) | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación MTM2012-32706 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Signal Processing, 134 (may 2017), 130-138. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Piecewise linear functions | es |
dc.subject | Noisy signals | es |
dc.subject | Persistent homology | es |
dc.subject | Persistent Entropy | es |
dc.subject | Supervised classi cation | es |
dc.title | A new topological entropy-based approach for measuring similarities among piecewise linear functions | es |
dc.type | info:eu-repo/semantics/article | 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 | MTM2012-32706 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0165168416303486 | es |
dc.identifier.doi | 10.1016/j.sigpro.2016.12.006 | es |
idus.format.extent | 16 | es |
dc.journaltitle | Signal Processing | es |
dc.publication.volumen | 134 | es |
dc.publication.issue | may 2017 | es |
dc.publication.initialPage | 130 | es |
dc.publication.endPage | 138 | es |
dc.identifier.sisius | 21026845 | es |
dc.identifier.sisius | 21285880 | es |