Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation

@article{Zhang2020JointLP,
  title={Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation},
  author={Z. Zhang and Yan Zhang and Guangcan Liu and J. Tang and S. Yan and M. Wang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2020},
  volume={32},
  pages={952-970}
}
  • Z. Zhang, Yan Zhang, +3 authors M. Wang
  • Published 2020
  • Computer Science
  • IEEE Transactions on Knowledge and Data Engineering
Constrained Concept Factorization (CCF) yields the enhanced representation ability over CF by incorporating label information as additional constraints, but it cannot classify and group unlabeled data appropriately. [...] Key Method To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data.Expand
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