Orthogonal Matching Pursuit: A Brownian Motion Analysis

A well-known analysis of Tropp and Gilbert shows that orthogonal matching pursuit (OMP) can recover ak-sparsen-dimensional real vector fromm=4klog(n) noise-free linear measurements obtained through a random Gaussian measurement matrix with a probability that approaches one asn→∞. This work strengthens this result by showing that a lower number of measurements,m=2klog(n-k) , is in fact sufficient for asymptotic recovery. More generally, when the sparsity level satisfieskmin≤k≤kmaxbut is unknown,m=2kmaxlog(n-kmin) measurements is sufficient. Furthermore, this number of measurements is also sufficient for detection of the sparsity pattern (support) of the vector with measurement errors provided the signal-to-noise ratio (SNR) scales to infinity. The scalingm=2klog(n-k) exactly matches the number of measurements required by the more complex lasso method for signal recovery with a similar SNR scaling.