Compressed Sensing With Cross Validation.

Compressed sensing (CS) decoding algorithms can efficiently recover an N-dimensional real-valued vector x to within a factor of its best k-term approximation by taking m = O(k log N/k) measurements y = Φx. If the sparsity or approximate sparsity level of x were known, then this theoretical guarantee would imply quality assurance of the resulting estimate. However, because the underlying sparsity of the signal x is unknown, the quality of a CS estimate ... using m measurements is not assured. It is nevertheless shown in this paper that sharp bounds on the error ||x - ...||𝓁N2 can be achieved with no effort. More precisely, suppose that a maximum number measurements m is preimposed. One can reserve 10 log p of these m measurements and compute a sequence of possible estimates (...j)pj=1 to x from the m - 10 log p remaining measurements; errors ||x - ...j||𝓁N2 for j = 1,..., p can then be bounded high probability. As a consequence, numerical upper and lower bounds on the error between x and the best k-term approximation to x can be estimated for p values of k with almost no cost. observation has applications outside CS as well. [ABSTRACT FROM AUTHOR]