AMP-MMV is a Bayesian message passing algorithm that solves the multiple measurement vector (MMV) problem, in which a matrix of noisy measurements, Y, is acquired from a sparse signal matrix, X, through the linear measurement process Y = AX + E, where A is a measurement matrix (typically with more columns than rows), and E is corrupting noise. Each column in X can represent the value of some time-varying signal at a particular instant in time, or can represent a time-invariant signal acquired in one of multiple channels. The unique feature of the MMV problem is the assumption that each column of X shares the same support, i.e., X is row-sparse.

AMP-MMV has been implemented in MATLAB, and has been shown to work extremely quickly, requiring only simple matrix-vector products to perform its computations. Click here to try it out.

AMP-MMV: An algorithm for efficient high-dimensional inference in the multiple measurement vector (MMV) problem


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