A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries
IEEE Transactions on Information Theory(2017)
摘要
We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements. Our main result is a strategy to approximately recover the unknown dictionary using an efficient algorithm. Our algorithm is a clustering-style procedure, where each cluster is used to estimate a dictionary element. The resulting solution can often be further cleaned up to obtain a high accuracy estimate, and we provide one simple scenario where $\\ell _{1}$ -regularized regression can be used for such a second stage.
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关键词
Dictionaries,Encoding,Clustering algorithms,Sparse matrices,Optimization,Context,Blind source separation
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