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- W2539576140 abstract "When there is no sufficient labeled instances, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled instances are used to improve the performance. In this paper, we propose a dimensionality reduction method called semi-supervised TransductIve Discriminant Analysis (TIDA) which preserves the global and geometrical structure of the unlabeled instances in addition to separating labeled instances in different classes from each other. The proposed algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that TIDA is superior to many relevant dimensionality reduction methods." @default.
- W2539576140 created "2016-11-04" @default.
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- W2539576140 date "2010-11-01" @default.
- W2539576140 modified "2023-09-28" @default.
- W2539576140 title "Semi-supervised Transductive Discriminant Analysis" @default.
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- W2539576140 doi "https://doi.org/10.1109/iske.2010.5680867" @default.
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