Non-Negative Semi-Supervised Learning

  title={Non-Negative Semi-Supervised Learning},
  author={Changhu Wang and Shuicheng Yan and Lei Zhang and HongJiang Zhang},
The contributions of this paper are three-fold. First, we present a general formulation for reaping the benefits from both non-negative data factorization and semi-supervised learning, and the solution naturally possesses the characteristics of sparsity, robustness to partial occlusions, and greater discriminating power via extra unlabeled data. Then, an efficient multiplicative updating procedure is proposed along with its theoretic justification of the algorithmic convergency. Finally, the… CONTINUE READING
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