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Graph-based semi-supervised learning |
Changshui ZHANG( ), Fei WANG |
| State Key Laboratory of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation,Tsinghua University, Beijing 100084, China |
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Abstract The recent years have witnessed a surge of interests in graph-based semi-supervised learning (GBSSL). In this paper, we will introduce a series of works done by our group on this topic including: 1) a method called linear neighborhood propagation (LNP) which can automatically construct the optimal graph; 2) a novel multilevel scheme to make our algorithm scalable for large data sets; 3) a generalized point charge scheme for GBSSL; 4) a multilabel GBSSL method by solving a Sylvester equation; 5) an information fusion framework for GBSSL; and 6) an application of GBSSL on fMRI image segmentation.
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| Keywords
graph-based semi-supervised learning (GBSSL)
linear neighborhood propagation (LNP)
point charge model
fMRI image segmentation
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Corresponding Author(s):
ZHANG Changshui,Email:zcs@mail.tsinghua.edu.cn
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Issue Date: 05 March 2011
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