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• W2086476397 abstract "We consider the problem of detecting a target signature which is known (up to an amplitude factor) to belong to a (possibly very) large library of signatures. Thus we know how each signature to be detected looks like, but we do not know which one is activated under H1. We propose a minimax approach for this problem aimed at maximizing the worst detection performance. Optimization issues and connections with One-Class classifiers are discussed and illustrated geometrically. Numerical results comparing the proposed approach to the classical sparse-coding dictionary learning technique K-SVD are provided on astrophysical hyperspectral data." @default.
• W2086476397 created "2016-06-24" @default.
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• W2086476397 date "2013-05-01" @default.
• W2086476397 modified "2023-10-14" @default.
• W2086476397 title "Minimax sparse detection based on one-class classifiers" @default.
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• W2086476397 doi "https://doi.org/10.1109/icassp.2013.6638726" @default.
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