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• W2046670215 abstract "Dictionary learning techniques have gained tremendous success in many classification problems. Inspired by the dirty model for multi-task regression problems, we proposed a novel method called group-structured dirty dictionary learning (GDDL) that incorporates the group structure (for each task) with the dirty model (across tasks) in the dictionary training process. Its benefits are two-fold: 1) the group structure enforces implicitly the label consistency needed between dictionary atoms and training data for classification; and 2) for each class, the dirty model separates the sparse coefficients into ones with shared support and unique support, with the first set being more discriminative. We use proximal operators and block coordinate decent to solve the optimization problem. GDDL has been shown to give state-of-art result on both synthetic simulation and two face recognition datasets." @default.
• W2046670215 created "2016-06-24" @default.
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• W2046670215 date "2014-10-01" @default.
• W2046670215 modified "2023-09-27" @default.
• W2046670215 title "Group structured dirty dictionary learning for classification" @default.
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• W2046670215 doi "https://doi.org/10.1109/icip.2014.7025029" @default.
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