dc.creatorVega-Oliveros, Didier Augusto
dc.creatorBerton, Lilian
dc.creatorEberle, André Mantini
dc.creatorLopes, Alneu de Andrade
dc.creatorLiang, Zhao
dc.date.accessioned2014-05-14T18:19:20Z
dc.date.accessioned2018-07-04T16:47:58Z
dc.date.available2014-05-14T18:19:20Z
dc.date.available2018-07-04T16:47:58Z
dc.date.created2014-05-14T18:19:20Z
dc.date.issued2014
dc.identifierJournal of Physics: Conference Series, Bristol, v.490, p.012022-1-012022-4, 2014
dc.identifierhttp://www.producao.usp.br/handle/BDPI/44841
dc.identifier10.1088/1742-6596/490/1/012022
dc.identifierhttp:dx.doi.org/10.1088/1742-6596/490/1/012022
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1640526
dc.description.abstractSemi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of the available data by identifying classes or groups regardless data distribution and representing submanifold in Euclidean space. Most of methods used in literature for SSL classification do not worry about graph construction. However, regular graphs can obtain better classification accuracy compared to traditional methods such as k-nearest neighbor (kNN), since kNN benefits the generation of hubs and it is not appropriate for high-dimensionality data. Nevertheless, methods commonly used for generating regular graphs have high computational cost. We tackle this problem introducing an alternative method for generation of regular graphs with better runtime performance compared to methods usually find in the area. Our technique is based on the preferential selection of vertices according some topological measures, like closeness, generating at the end of the process a regular graph. Experiments using the global and local consistency method for label propagation show that our method provides better or equal classification rate in comparison with kNN.
dc.languageeng
dc.publisherIOP Publishing
dc.publisherBristol
dc.relationJournal of Physics: Conference Series
dc.rightshttp://creativecommons.org/licenses/by/3.0/br/
dc.rightsopenAccess
dc.subjectComputers in experimental physics
dc.subjectCombinatorics; graph theory
dc.titleRegular graph construction for semi-supervised learning
dc.typeArtículos de revistas


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