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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SPARS11, Edinburgh, UK, June 27-29, 2011. 2011
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https://hal.inria.fr/hal-00705943
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : vendredi 8 juin 2012 - 15:23:54
Dernière modification le : lundi 2 octobre 2017 - 16:06:02

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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. SPARS11, Edinburgh, UK, June 27-29, 2011. 2011. 〈hal-00705943〉

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