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ICASSP
2011
IEEE

A clustering based framework for dictionary block structure identification

8 years 6 months ago
A clustering based framework for dictionary block structure identification
Sparse representations over redundant dictionaries offer an efficient paradigm for signal representation. Recently block-sparsity has been put forward as a prior condition for some sparse representation applications, where the coefficients of the sparse representation occur in blocks rather than being distributed randomly over the sparse vector. Block-sparse representation algorithms, which are extensions of the regular sparse representation algorithms have been developed. However, these algorithms work under the assumption that both the dictionary and its corresponding block structure are known. In this paper, we consider the problem of recovering the optimally blocksparsifying block structure for a given data set and dictionary pair. We propose a block structure identification framework employing a clustering step which can be realized using the standard clustering schemes from the literature. The block structure identification algorithm works efficiently, and for synthetically...
Ender M. Eksioglu
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Ender M. Eksioglu
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