Particle Competition and Cooperation in Networks for Semi-Supervised Learning
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
23/08/2013
23/08/2013
2012
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Resumo |
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. State of Sao Paulo Research Foundation (FAPESP) Brazilian National Council of Technological and Scientific Development (CNPq) |
Identificador |
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, LOS ALAMITOS, v. 24, n. 9, pp. 1686-1698, SEP, 2012 1041-4347 http://www.producao.usp.br/handle/BDPI/32691 10.1109/TKDE.2011.119 |
Idioma(s) |
eng |
Publicador |
IEEE COMPUTER SOC LOS ALAMITOS |
Relação |
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
Direitos |
restrictedAccess Copyright IEEE COMPUTER SOC |
Palavras-Chave | #SEMI-SUPERVISED LEARNING #PARTICLES COMPETITION AND COOPERATION #NETWORK-BASED METHODS #LABEL PROPAGATION #COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE #COMPUTER SCIENCE, INFORMATION SYSTEMS #ENGINEERING, ELECTRICAL & ELECTRONIC |
Tipo |
article original article publishedVersion |