Optimized projections for compressed sensing via rank-constrained nearest correlation matrix
نویسندگان
چکیده
منابع مشابه
Optimized projections for compressed sensing via rank-constrained nearest correlation matrix
Optimizing the acquisition matrix is useful for compressed sensing of signals that are sparse in overcomplete dictionaries, because the acquisition matrix can be adapted to the particular correlations of the dictionary atoms. In this paper a novel formulation of the optimization problem is proposed, in the form of a rank-constrained nearest correlation matrix problem. Furthermore, improvements ...
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متن کاملSupplemental Materials for “ Spectral Compressed Sensing via Structured Matrix Completion ”
This supplemental document presents details concerning analytical derivations that support the theorems made in the main text " Spectral Compressed Sensing via Structured Matrix Completion " , accepted to the 30th International Conference on Machine Learning (ICML 2013). One can find here the detailed proof of Theorems 1-3.
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ژورنال
عنوان ژورنال: Applied and Computational Harmonic Analysis
سال: 2014
ISSN: 1063-5203
DOI: 10.1016/j.acha.2013.08.005