Particle Competition and Cooperation in Networks for Semi-Supervised Learning
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
---|---|
Data(s) |
30/09/2013
20/05/2014
30/09/2013
20/05/2014
01/09/2012
|
Resumo |
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a divide-and-conquer effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. |
Formato |
1686-1698 |
Identificador |
http://dx.doi.org/10.1109/TKDE.2011.119 IEEE Transactions on Knowledge and Data Engineering. Los Alamitos: IEEE Computer Soc, v. 24, n. 9, p. 1686-1698, 2012. 1041-4347 http://hdl.handle.net/11449/24904 10.1109/TKDE.2011.119 WOS:000306557800011 |
Idioma(s) |
eng |
Publicador |
Institute of Electrical and Electronics Engineers (IEEE), Computer Soc |
Relação |
IEEE Transactions on Knowledge and Data Engineering |
Direitos |
closedAccess |
Palavras-Chave | #Semi-supervised learning #particles competition and cooperation #network-based methods #label propagation |
Tipo |
info:eu-repo/semantics/article |