Semi-supervised learning guided by the modularity measure in complex networks
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
30/10/2013
30/10/2013
2012
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Resumo |
Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method. State of Sao Paulo Research Foundation (FAPESP) Brazilian National Council of Technological and Scientific Development (CNPq) |
Identificador |
NEUROCOMPUTING, AMSTERDAM, v. 78, n. 1, Special Issue, p. 30-37, FEB 15, 2012 0925-2312 http://www.producao.usp.br/handle/BDPI/36828 10.1016/j.neucom.2011.04.042 |
Idioma(s) |
eng |
Publicador |
ELSEVIER SCIENCE BV AMSTERDAM |
Relação |
NEUROCOMPUTING |
Direitos |
restrictedAccess Copyright ELSEVIER SCIENCE BV |
Palavras-Chave | #SEMI-SUPERVISED LEARNING #MODULARITY #COMPLEX NETWORKS #NETWORK REDUCTION #COMMUNITY STRUCTURE #RANDOM-WALKS #COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE |
Tipo |
article original article publishedVersion |