|High-dimensional rank-one nonsymmetric matrix decomposition: the spherical case
|Clément Luneau, Nicolas Macris, EPFL, Switzerland; Jean Barbier, ICTP, Italy
|L.7: High-dimensional Statistics
|Statistics and Learning Theory
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|We consider the problem of estimating a rank-one nonsymmetric matrix under additive white Gaussian noise. The matrix to estimate can be written as the outer product of two vectors and we look at the special case in which both vectors are uniformly distributed on spheres. We prove a replica-symmetric formula for the average mutual information between these vectors and the observations in the high-dimensional regime. This goes beyond previous results which considered vectors with independent and identically distributed elements. The method used can be extended to rank-one tensor problems.