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Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis
Alyson Fletcher · Sundeep Rangan

Wed Dec 09 07:00 PM -- 11:59 PM (PST) @ None #None

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for recovering sparse vectors from linear measurements. A well-known analysis of Tropp and Gilbert shows that OMP can recover a k-sparse n-dimensional real vector from m = 4k log(n) noise-free random linear measurements with a probability that goes to one as n goes to infinity. This work shows strengthens this result by showing that a lower number of measurements, m = 2k log(n-k), is in fact sufficient for asymptotic recovery. Moreover, 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 scaling m = 2k log(n-k) exactly matches the number of measurements required by the more complex lasso for signal recovery.

Author Information

Allie Fletcher (UCLA)
Sundeep Rangan (Qualcomm)

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