We generalize cyclic matching pursuit (CMP),propose an orthogonal variant,and examine their performance using multiscale time-frequency dictionariesin the sparse approximation of signals.Overall, we find that the cyclic approach of CMP produces signal models that have a much lower approximation errorthan existing greedy iterative descent methodssuch as matching pursuit (MP),and are competitive with models found using orthogonal MP (OMP), and orthogonal least squares (OLS).This implies that CMP is a strong alternative to the more computationally complex approaches of OMP and OLSfor modeling high-dimensional signals.
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