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- W2498838870 abstract "We develop Bayesian learning methods for low-rank matrix reconstruction and completion from linear measurements. For under-determined systems, the developed methods reconstruct low-rank matrices when neither the rank nor the noise power is known a priori . We derive relations between the proposed Bayesian models and low-rank promoting penalty functions. The relations justify the use of Kronecker structured covariance matrices in a Gaussian-based prior. In the methods, we use expectation maximization to learn the model parameters. The performance of the methods is evaluated through extensive numerical simulations on synthetic and real data." @default.
- W2498838870 created "2016-08-23" @default.
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- W2498838870 date "2016-10-15" @default.
- W2498838870 modified "2023-10-03" @default.
- W2498838870 title "Relevance Singular Vector Machine for Low-Rank Matrix Reconstruction" @default.
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- W2498838870 doi "https://doi.org/10.1109/tsp.2016.2597121" @default.
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