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- W2586917466 abstract "We consider the high-dimensional inference problem where the signal is a low-rank matrix which is corrupted by an additive Gaussian noise. Given a probabilistic model for the low-rank matrix, we compute the limit in the large dimension setting for the mutual information between the signal and the observations, as well as the matrix minimum mean square error, while the rank of the signal remains constant. This allows to locate the information-theoretic threshold for this estimation problem, i.e. the critical value of the signal intensity below which it is impossible to recover the low-rank matrix." @default.
- W2586917466 created "2017-02-17" @default.
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- W2586917466 date "2017-02-01" @default.
- W2586917466 modified "2023-09-22" @default.
- W2586917466 title "Fundamental limits of low-rank matrix estimation" @default.
- W2586917466 hasPublicationYear "2017" @default.
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