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Local-driven semi-supervised learning with multi-label
2009
2009 IEEE International Conference on Multimedia and Expo
In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data. Instead of using each datum as a vertex of graph, we encode each extracted local feature descriptor as a vertex, and then the labels for each vertex from the training data are derived based on the context among different training data, finally the decomposed labels on each vertex are further propagated to the unlabeled vertices based
doi:10.1109/icme.2009.5202790
dblp:conf/icmcs/LiYMK09
fatcat:sdp3q37elra3tjk7dbkzeolguq