On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements

18 Jun 2013Akshay SoniJarvis Haupt

Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adaptive linear observations, provided that the signals possess a sparse representation in some basis. Subsequent efforts have shown that the performance of CS can be improved by exploiting additional structure in the locations of the nonzero signal coefficients during inference, or by utilizing some form of data-dependent adaptive measurement focusing during the sensing process... (read more)

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