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• W3080456940 abstract "We consider a generalization of an important class of high-dimensional inference problems, namely spiked symmetric matrix models, often used as probabilistic models for principal component analysis. Such paradigmatic models have recently attracted a lot of attention from a number of communities due to their phenomenological richness with statistical-to-computational gaps, while remaining tractable. We rigorously establish the information-theoretic limits through the proof of single-letter formulas for the mutual information and minimum mean-square error. On a technical side we improve the recently introduced adaptive interpolation method, so that it can be used to study low-rank models (i.e., estimation problems of tall matrices) in full generality, an important step towards the rigorous analysis of more complicated inference and learning models." @default.
• W3080456940 created "2020-09-01" @default.
• W3080456940 creator A5057057294 @default.
• W3080456940 creator A5084396713 @default.
• W3080456940 date "2020-06-01" @default.
• W3080456940 modified "2023-09-30" @default.
• W3080456940 title "Information-theoretic limits of a multiview low-rank symmetric spiked matrix model" @default.
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• W3080456940 doi "https://doi.org/10.1109/isit44484.2020.9173970" @default.
• W3080456940 hasPublicationYear "2020" @default.
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