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• W4300605969 abstract "A host of problems involve the recovery of structured signals from a dimensionality reduced representation such as a random projection; examples include sparse signals (compressive sensing) and low-rank matrices (matrix completion). Given the wide range of different recovery algorithms developed to date, it is natural to ask whether there exist universal algorithms for recovering structured signals from their linear projections. We recently answered this question in the affirmative in the noise-free setting. In this paper, we extend our results to the case of noisy measurements." @default.
• W4300605969 created "2022-10-03" @default.
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• W4300605969 date "2012-05-21" @default.
• W4300605969 modified "2023-09-27" @default.
• W4300605969 title "Minimum Complexity Pursuit: Stability Analysis" @default.
• W4300605969 doi "https://doi.org/10.48550/arxiv.1205.4673" @default.
• W4300605969 hasPublicationYear "2012" @default.
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