Sparse recovery under weak moment assumptions

作者:Lecue Guillaume*; Mendelson Shahar
来源:Journal of the European Mathematical Society, 2017, 19(3): 881-904.
DOI:10.4171/JEMS/682

摘要

We prove that iid random vectors that satisfy a rather weak moment assumption can be used as measurement vectors in Compressed Sensing, and the number of measurements required for exact reconstruction is the same as the best possible estimate - exhibited by a random Gaussian matrix. We then show that this moment condition is necessary, up to a log log factor. In addition, we explore the Compatibility Condition and the Restricted Eigenvalue Condition in the noisy setup, as well as properties of neighbourly random polytopes.