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- W1979320848 abstract "Recently, semi-supervised learning, which uses unlabeled samples and supervised information together in learning process, has received much attention. Compared with class labels, pairwise constraints is a kind of supervised information which are more easily to obtain. In this paper, a new algorithm is proposed, called as SSGL, which preserves both the global (intrinsic) and local structure of the original data (both labeled and unlabeled data) and incorporates the structure of the pairwise constraints in the projected low-dimensional space. In this way, the novel algorithm intends to find a better discriminative projection. Experiment results on Yale and ORL face databases show its effectiveness." @default.
- W1979320848 created "2016-06-24" @default.
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- W1979320848 date "2012-07-01" @default.
- W1979320848 modified "2023-10-05" @default.
- W1979320848 title "Semi-supervised dimensionality reduction based on global and local scatter" @default.
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- W1979320848 doi "https://doi.org/10.1109/iciea.2012.6361056" @default.
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