A new topological entropy-based approach for measuring similarities among piecewise linear functions
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 implementat...
| Autores: | , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/87688 |
| Acceso en línea: | https://hdl.handle.net/11441/87688 https://doi.org/10.1016/j.sigpro.2016.12.006 |
| Access Level: | acceso abierto |
| Palabra clave: | Piecewise linear functions Noisy signals Persistent homology Persistent Entropy Supervised classi cation |
| Sumario: | 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%) |
|---|