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• W2130658544 abstract "Sparse representations have emerged as a powerful approach for encoding images in a large class of machine recognition problems including face recognition. These methods rely on the use of an over-complete basis set for representing an image. This often assumes the availability of a large number of labeled training images, especially for high dimensional data. In many practical problems, the number of labeled training samples are very limited leading to significant degradations in classification performance. To address the problem of lack of training samples, we propose a semi-supervised algorithm that labels the unlabeled samples through a multi-stage label propagation combined with sparse representation. In this representation, each image is decomposed as a linear combination of its nearest basis images, which has the advantage of both locality and sparsity. Extensive experiments on publicly available face databases show that the results are significantly better compared to state-of-the-art face recognition methods in semi-supervised setting and are on par with fully supervised techniques." @default.
• W2130658544 created "2016-06-24" @default.
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• W2130658544 date "2013-11-01" @default.
• W2130658544 modified "2023-09-26" @default.
• W2130658544 title "Sparse Representation Based Face Recognition with Limited Labeled Samples" @default.
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• W2130658544 doi "https://doi.org/10.1109/acpr.2013.38" @default.
• W2130658544 hasPublicationYear "2013" @default.
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