Manifold Correlation Graph for Semi-Supervised Learning

dc.contributor.authorValem, Lucas Pascotti [UNESP]
dc.contributor.authorPedronette, Daniel C. G. [UNESP]
dc.contributor.authorBreve, Fabricio [UNESP]
dc.contributor.authorGuilherme, Ivan Rizzo [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-06T16:55:20Z
dc.date.available2019-10-06T16:55:20Z
dc.date.issued2018-10-10
dc.description.abstractDue to the growing availability of unlabeled data and the difficulties in obtaining labeled data, the use of semi-supervised learning approaches becomes even more promising. The capacity of taking into account the dataset structure is of crucial relevance for effectively considering the unlabeled data. In this paper, a novel classifier is proposed through a manifold learning approach. The graph is constructed based on a new hybrid similarity measure which encodes both supervised and unsupervised information. Next, strongly connected components are computed and used to analyze the dataset manifold. The classification is performed through a voting scheme based on primary (labeled) and secondary (unlabeled) voters. An experimental evaluation is conducted, considering various datasets, diverse situations of training/test dataset sizes and comparison with baselines. The proposed method achieved positive results in most of situations.en
dc.description.affiliationDepartment of Statistics São Paulo State University (UNESP)
dc.description.affiliationUnespDepartment of Statistics São Paulo State University (UNESP)
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2018.8489487
dc.identifier.citationProceedings of the International Joint Conference on Neural Networks, v. 2018-July.
dc.identifier.doi10.1109/IJCNN.2018.8489487
dc.identifier.scopus2-s2.0-85056555894
dc.identifier.urihttp://hdl.handle.net/11449/189882
dc.language.isoeng
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.titleManifold Correlation Graph for Semi-Supervised Learningen
dc.typeTrabalho apresentado em evento

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