Explicit Shift-Invariant Dictionary Learning

Signal Processing Letters, IEEE  (2014)

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摘要
In this letter we give efficient solutions to the construction of structured dictionaries for sparse representations. We study circulant and Toeplitz structures and give fast algorithms based on least squares solutions. We take advantage of explicit circulant structures and we apply the resulting algorithms to shift-invariant learning scenarios. Synthetic experiments and comparisons with state-of-the-art methods show the superiority of the proposed methods.
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关键词
learning (artificial intelligence),least mean squares methods,toeplitz structures,circulant structures,explicit shift-invariant dictionary learning,least squares solutions,shift-invariant learning scenarios,sparse representations,structured dictionaries,dictionary learning,shift-invariant learning,learning artificial intelligence
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