Uniqueness conditions for low-rank matrix recovery
نویسندگان
چکیده
منابع مشابه
Uniqueness Conditions For Low-Rank Matrix Recovery
Low-rank matrix recovery addresses the problem of recovering an unknown low-rank matrix from few linear measurements. Nuclear-norm minimization is a tractable approach with a recent surge of strong theoretical backing. Analagous to the theory of compressed sensing, these results have required random measurements. For example, m ≥ Cnr Gaussian measurements are sufficient to recover any rank-r n ...
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Low-rank matrix recovery addresses the problem of recovering an unknown low-rank matrix from few linear measurements. Nuclear-norm minimization is a tractible approach with a recent surge of strong theoretical backing. Analagous to the theory of compressed sensing, these results have required random measurements. For example, m ≥ Cnr Gaussian measurements are sufficient to recover any rank-r n×...
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and Applied Analysis 3 If there does not exist such y for some X as above, we set γ s (A, β) = +∞ and to be compatible with the special case given by [24] we write γ s (A), γ s (A) instead of γ s (A, +∞), γ s (A, +∞), respectively. From the above definition, we easily see that the set of values that γ takes is closed. Thus, when γ s (A, β) < +∞, for every matrix X ∈ Rm×n with s nonzero singular...
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ژورنال
عنوان ژورنال: Applied and Computational Harmonic Analysis
سال: 2012
ISSN: 1063-5203
DOI: 10.1016/j.acha.2012.04.002