| Texto completo | |
| Autor(es): |
Berton, Lilian
;
Faleiros, Thiago de Paulo
;
Valejo, Alan
;
Valverde-Rebaza, Jorge
;
Lopes, Alneu de Andrade
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | Neurocomputing; v. 226, p. 238-248, FEB 22 2017. |
| Citações Web of Science: | 3 |
| Resumo | |
Graph-based semi-supervised learning (SSL) provides a powerful framework for the modeling of manifold structures in high-dimensional spaces. Additionally, graph representation is effective for the propagation of the few initial labels existing in training data. Graph-based SSL requires robust graphs as input for an accurate data mining task, such as classification. In contrast to most graph construction methods, which ignore the labeled instances available in SSL scenarios, a previous study proposed a graph-construction method, named GBILI, to exploit the informativeness conveyed by such instances available in a semi-supervised classification domain. Here, we have improved the method proposing an optimized algorithm referred to as Robust Graph that Considers Labeled Instances (RGCLI) for the generation of more robust graphs. The contributions of this paper are threefold: i) reduction of GBILI time complexity from quadratic to O(nklogn). This enhancement allows addressing large datasets; demonstration of RGCLI mathematical properties, proving the constructed graph is an optimal graph to model the smoothness assumption of SSL; and evaluation of the efficacy of the proposed approach in a comprehensive semi-supervised classification scenario with several datasets, including an image segmentation task, which needs a large graph to represent the image. Such experiments show the use of labeled vertices in the graph construction process improves the graph topology, hence, the learning task in which it will be employed. (AU) | |
| Processo FAPESP: | 11/23689-9 - Propagação em Grafos Bipartidos para Extração de Tópicos em Fluxo de Dados |
| Beneficiário: | Thiago de Paulo Faleiros |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 11/21880-3 - Construção de redes para o aprendizado semissupervisionado |
| Beneficiário: | Lilian Berton |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 15/14228-9 - Análise e Mineração de Redes Sociais |
| Beneficiário: | Alneu de Andrade Lopes |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/12191-5 - Mineração do Comportamento de Usuários em Redes Sociais baseadas em Localização |
| Beneficiário: | Jorge Carlos Valverde Rebaza |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |