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• W2550781223 abstract "We present a dictionary learning algorithm that aims to reduce the size of the dictionary to a parsimonious value during the learning process. The sparse coding step uses a weighted Orthogonal Matching Pursuit favoring atoms that enter more representations. The dictionary update step optimizes a regularized error, encouraging the apparition of zero rows in the representation matrix; the corresponding unused atoms are eliminated. The algorithm is extended to the case of incomplete data. Besides dictionary learning, the algorithm is also shown to be useful for finding low-dimensional subspaces. Such versatility is a feature with little precedent. Numerical examples show good convergence properties." @default.
• W2550781223 created "2016-11-30" @default.
• W2550781223 creator A5023994037 @default.
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• W2550781223 date "2016-09-01" @default.
• W2550781223 modified "2023-10-17" @default.
• W2550781223 title "Low dimensional subspace finding via size-reducing dictionary learning" @default.
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• W2550781223 doi "https://doi.org/10.1109/mlsp.2016.7738900" @default.
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