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- W4200106960 abstract "This paper addresses a robust convolutional dictionary learning method against outliers. Convolutional dictionary learning approximates a signal with the sum of dictionary filters and corresponding coefficients, and its cost function consists of the weighted sum of the two terms: error and regularization terms. Many studies employ the l<inf>2</inf> and the l<inf>1</inf> norms for the former and the latter respectively, and to increase the robustness, the l<inf>1</inf> norm is substituted for the error term. For such optimization problems with the sum of the two convex terms, the proximal gradient method is a powerful solver; however, it is not applicable for the two l<inf>1</inf> terms, of which gradient is not continuous at any point. This paper tries to apply the Moreau envelope for the l<inf>1</inf> error term, and the l<inf>1</inf> error is expressed as Huber error function, which is differentiable and Lipschitz continuous. Experimental results show that dictionaries generated with the proposed method are robuster than those with the l<inf>2</inf> error term." @default.
- W4200106960 created "2021-12-31" @default.
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- W4200106960 date "2021-11-16" @default.
- W4200106960 modified "2023-09-26" @default.
- W4200106960 title "Convolutional Dictionary Learning with Huber Error and l1 Regularization Terms" @default.
- W4200106960 doi "https://doi.org/10.1109/ispacs51563.2021.9651025" @default.
- W4200106960 hasPublicationYear "2021" @default.
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