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- W801970942 abstract "This paper proposes a novel discriminant semi-supervised feature extraction for generic classification and recognition tasks. The paper has two main contributions. First, we propose a flexible linear semi-supervised feature extraction method that seeks a non-linear subspace that is close to a linear one. The proposed method is based on a criterion that simultaneously exploits the discrimination information provided by the labeled samples, maintains the graph-based smoothness associated with all samples, regularizes the complexity of the linear transform, and minimizes the discrepancy between the unknown linear regression and the unknown non-linear projection. Second, we provide extensive experiments on four benchmark databases in order to study the performance of the proposed method. These experiments demonstrate much improvement over the state-of-the-art algorithms that are either based on label propagation or semi-supervised graph-based embedding." @default.
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- W801970942 date "2015-01-01" @default.
- W801970942 modified "2023-09-23" @default.
- W801970942 title "A Flexible Semi-supervised Feature Extraction Method for Image Classification" @default.
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- W801970942 doi "https://doi.org/10.1007/978-3-319-16634-6_10" @default.
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