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- W1996222011 abstract "Exploiting label dependency is a key challenge in multi-label learning, and current methods solve this problem mainly by training models on the combination of related labels and original features. However, label dependency cannot be exploited dynamically and mutually in this way. Therefore, we propose a novel paradigm of leveraging label dependency in an iterative way. Specifically, each label's prediction will be updated and also propagated to other labels via an random walk with restart process. Meanwhile, the label propagation is implemented as a supervised learning procedure via optimizing a loss function, thus more appropriate label dependency can be learned. Extensive experiments are conducted, and the results demonstrate that our method can achieve considerable improvements in terms of several evaluation metrics." @default.
- W1996222011 created "2016-06-24" @default.
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- W1996222011 date "2013-12-01" @default.
- W1996222011 modified "2023-10-16" @default.
- W1996222011 title "Leveraging Supervised Label Dependency Propagation for Multi-label Learning" @default.
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- W1996222011 doi "https://doi.org/10.1109/icdm.2013.143" @default.
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