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• W2912696054 abstract "In this paper, we propose a novel approach to convolutional sparse representation with the aim of resolving the dictionary learning problem. The proposed method, referred to as the adaptive alternating direction method of multipliers (AADMM), employs constraints comprising non-convex, non-smooth terms, such as the l0 -norm imposed on the coefficients and the unit-norm sphere imposed on the length of each dictionary element. The proposed scheme incorporates a novel parameter adaption scheme that enables ADMM to achieve convergence more quickly, as evidenced by numerical and theoretical analysis. In experiments involving image signal applications, the dictionaries learned using AADMM outperformed those learned using comparable dictionary learning methods." @default.
• W2912696054 created "2019-02-21" @default.
• W2912696054 creator A5088682683 @default.
• W2912696054 date "2019-07-01" @default.
• W2912696054 modified "2023-10-12" @default.
• W2912696054 title "Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation" @default.
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• W2912696054 doi "https://doi.org/10.1109/tip.2019.2896541" @default.
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