Relevance Singular Vector Machine for low-rank matrix sensing

Martin Sundin; Saikat Chatterjee; Magnus Jansson; Cristian R. Rojas

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arXiv · 1407.0013

Relevance Singular Vector Machine for low-rank matrix sensing

Martin Sundin·Saikat Chatterjee·Magnus Jansson·Cristian R. Rojas

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Abstract

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.

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BibTeX RIS

Martin Sundin, Saikat Chatterjee, Magnus Jansson, Cristian R. Rojas. 2014-06-30. Relevance Singular Vector Machine for low-rank matrix sensing. https://arxiv.org/abs/1407.0013

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