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Greedy Dictionary Selection for Sparse Representation
2011
IEEE Journal on Selected Topics in Signal Processing
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal dimension, can exactly represent or wellapproximate the signals of interest. We formulate both the selection of the dictionary columns and the sparse representation of signals as a joint combinatorial optimization problem. The proposed
doi:10.1109/jstsp.2011.2161862
fatcat:wppvskirxvdn3oas6ca6df2qii