  This paper considers signal recovery in the framework of cumulative
coherence. First, we show that the Lasso estimator and the Dantzig selector
exhibit similar behavior under the cumulative coherence. Then we estimate the
approximation equivalence between the Lasso and the Dantzig selector by
calculating prediction loss difference under the condition of cumulative
coherence. And we also prove that the cumulative coherence implies the
restricted eigenvalue condition. Last, we illustrate the advantages of
cumulative coherence condition for three class matrices, in terms of the
recovery performance of sparse signals via extensive numerical experiments.
