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• W2912863276 abstract "Recently, the sparse coding method has been successfully applied in the low-rank subspace clustering algorithm. In these methods, the image is represented as a low-rank linear combination of the atoms of the sparse dictionary, and the coefficient matrix is used to compute the similarity matrix for spectral clustering. However, the low-rank representation obtained by the subspace clustering may not be enough to describe the data structure. In this paper, we propose a new model by combining sparse coding and low-rank subspace clustering in the objective function simultaneously. The new model seeks for solutions which have sparse representation and low-rank representation as close as possible. In this case, the new representation obtained by our method is both sparse and low-rank. An efficient iteratively algorithm is also proposed to solve this model. Extensive experiments on several databases demonstrate that our method performs better than state-of-the-art methods." @default.
• W2912863276 created "2019-02-21" @default.
• W2912863276 creator A5044423719 @default.
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• W2912863276 date "2018-11-01" @default.
• W2912863276 modified "2023-09-25" @default.
• W2912863276 title "A New Representation for Data: Sparse and Low-Rank" @default.
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• W2912863276 doi "https://doi.org/10.1109/cac.2018.8623248" @default.
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