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- W1837471008 abstract "We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K << N. In contrast to the existing work in the sparse approximation and compressive sensing literature on block sparsity, no prior knowledge of the locations and sizes of the clusters is assumed. We prove that O (K + C log(N/C))) random projections are sufficient for (K,C)-model sparse signal recovery based on subspace enumeration. We also provide a robust polynomialtime recovery algorithm for (K,C)-model sparse signals with provable estimation guarantees." @default.
- W1837471008 created "2016-06-24" @default.
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- W1837471008 date "2009-12-21" @default.
- W1837471008 modified "2023-10-16" @default.
- W1837471008 title "Recovery of Clustered Sparse Signals from Compressive Measurements" @default.
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- W1837471008 doi "https://doi.org/10.21236/ada520218" @default.
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