Manifold Correlation Graph for Semi-Supervised Learning

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorValem, Lucas Pascotti [UNESP]-
Autor(es): dc.creatorPedronette, Daniel C. G. [UNESP]-
Autor(es): dc.creatorBreve, Fabricio [UNESP]-
Autor(es): dc.creatorGuilherme, Ivan Rizzo [UNESP]-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2022-02-22T00:57:08Z-
Data de disponibilização: dc.date.available2022-02-22T00:57:08Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2018-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209623-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209623-
Descrição: dc.descriptionDue 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionPetrobras-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro, Brazil-
Descrição: dc.descriptionFAPESP: 2017/02091-4-
Descrição: dc.descriptionFAPESP: 2016/05669-4-
Descrição: dc.descriptionFAPESP: 2013/08645-0-
Descrição: dc.descriptionCNPq: 308194/2017-9-
Descrição: dc.descriptionPetrobras: 2014/00545-0-
Formato: dc.format7-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2018 International Joint Conference On Neural Networks (ijcnn)-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleManifold Correlation Graph for Semi-Supervised Learning-
Aparece nas coleções:Repositório Institucional - Unesp

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