Manifold Correlation Graph for Semi-Supervised Learning
dc.contributor.author | Valem, Lucas Pascotti [UNESP] | |
dc.contributor.author | Pedronette, Daniel C. G. [UNESP] | |
dc.contributor.author | Breve, Fabricio [UNESP] | |
dc.contributor.author | Guilherme, Ivan Rizzo [UNESP] | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2019-10-06T16:55:20Z | |
dc.date.available | 2019-10-06T16:55:20Z | |
dc.date.issued | 2018-10-10 | |
dc.description.abstract | Due 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.affiliation | Department of Statistics São Paulo State University (UNESP) | |
dc.description.affiliationUnesp | Department of Statistics São Paulo State University (UNESP) | |
dc.identifier | http://dx.doi.org/10.1109/IJCNN.2018.8489487 | |
dc.identifier.citation | Proceedings of the International Joint Conference on Neural Networks, v. 2018-July. | |
dc.identifier.doi | 10.1109/IJCNN.2018.8489487 | |
dc.identifier.scopus | 2-s2.0-85056555894 | |
dc.identifier.uri | http://hdl.handle.net/11449/189882 | |
dc.language.iso | eng | |
dc.relation.ispartof | Proceedings of the International Joint Conference on Neural Networks | |
dc.rights.accessRights | Acesso aberto | |
dc.source | Scopus | |
dc.title | Manifold Correlation Graph for Semi-Supervised Learning | en |
dc.type | Trabalho apresentado em evento |