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- W37260452 abstract "AbstractConventional semi-supervised learning leverages the unlabeled data by intensive exploring the pairwise relation among the data points. However, it is well known that such relation cannot capture the complex interaction in many real-world applications. To address this problem, we proposed in this paper a new approach to effectively modeling the labeled and unlabeled data by local tangent space alignment, which is superior due to the properties of invariant to shift and scale. We apply the local tangent space alignment to semi-supervised learning tasks including semi-supervised classification. The experiments compared with state-of-the-art semi-supervised learning methods demonstrated the effectiveness of the proposed approach.Keywordssemi-supervised learningmanifold learninglocal tangent space alignment" @default.
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- W37260452 date "2011-01-01" @default.
- W37260452 modified "2023-09-22" @default.
- W37260452 title "Semi-supervised Local Tangent Space Alignment" @default.
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- W37260452 doi "https://doi.org/10.1007/978-3-642-19712-3_78" @default.
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