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Relevance Singular Vector Machine for low-rank matrix sensing
In this paper we develop a new Bayesian inference method for low rank matrix
reconstruction. We call the new method the Relevance Singular Vector Machine
(RSVM) where appropriate priors are defined on the singular vectors of the
underlying matrix to promote low rank. To accelerate computations, a
numerically efficient approximation is developed. The proposed algorithms are
applied to matrix completion and matrix reconstruction problems and their
performance is studied numerically.Comment: International Conference on Signal Processing and Communications
(SPCOM), 5 page
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