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Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Abstract : Assume a multichannel data matrix, which due to the column-wise dependencies, has low-rank and joint-sparse representation. This matrix wont have many degrees of freedom. Enormous developments over the last decade in areas of compressed sensing and low-rank matrix recovery, let us thinking of acquiring the whole matrix elements from very few non-adaptive linear measurements. This paper attempts to answer the following questions: what should be those measurements? How to design a computationally tractable algorithm to recover data from noisy measurements? Finally, how the recovery method performs, and is it stable for approximately low-rank or not exactly joint-sparse matrices?
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https://hal.inria.fr/hal-00705943
Contributor : Jules Espiau de Lamaestre <>
Submitted on : Friday, June 8, 2012 - 3:23:54 PM
Last modification on : Monday, October 2, 2017 - 4:06:02 PM

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  • HAL Id : hal-00705943, version 1

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Mohammad Golbabaee, Pierre Vandergheynst. Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements. 2011. ⟨hal-00705943⟩

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