dc.contributor.author | Ziraki, Najmeh | |
dc.contributor.author | Dornaika, Fadi | |
dc.contributor.author | Bosaghzadeh, Alireza | |
dc.date.accessioned | 2022-02-10T11:09:21Z | |
dc.date.available | 2022-02-10T11:09:21Z | |
dc.date.issued | 2022-02 | |
dc.identifier.citation | Neural Networks 146 : 174-180 (2022) | es_ES |
dc.identifier.issn | 1879-2782 | |
dc.identifier.uri | http://hdl.handle.net/10810/55414 | |
dc.description.abstract | Graph construction plays an essential role in graph-based label propagation since graphs give some information on the structure of the data manifold. While most graph construction methods rely on predefined distance calculation, recent algorithms merge the task of label propagation and graph construction in a single process. Moreover, the use of several descriptors is proved to outperform a single descriptor in representing the relation between the nodes. In this article, we propose a Multiple-View Consistent Graph construction and Label propagation algorithm (MVCGL) that simultaneously constructs a consistent graph based on several descriptors and performs label propagation over unlabeled samples. Furthermore, it provides a mapping function from the feature space to the label space with which we estimate the label of unseen samples via a linear projection. The constructed graph does not rely on a predefined similarity function and exploits data and label smoothness. Experiments conducted on three face and one handwritten digit databases show that the proposed method can gain better performance compared to other graph construction and label propagation methods. | es_ES |
dc.description.sponsorship | This work was partially funded by the Spanish Ministerio de
Ciencia, Innovación y Universidades, Spain, Programa Estatal de
I+D+i Orientada a los Retos de la Sociedad, RTI2018-101045-B-
C21, and the University of the Basque Country, GIU19/027 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/RTI2018-101045-B- C21 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | graph construction | es_ES |
dc.subject | graph-based data smoothness | es_ES |
dc.subject | information fusion | es_ES |
dc.subject | multi-view semi-supervised classification | es_ES |
dc.title | Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | es_ES |
dc.rights.holder | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0893608021004433?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.neunet.2021.11.015 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |