Sparse and Compressive Sensing Techniques
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Scope of this subject
This cluster of papers focuses on the theory and applications of compressed sensing, including sparse representation, signal recovery, convex optimization, matrix completion, dictionary learning, orthogonal matching pursuit, robust reconstruction, and sparsity in signal processing.
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Research vocabulary
compressed sensing; sparse signal recovery; sparse representation; signal reconstruction; orthogonal matching pursuit; restricted isometry property; l1-norm minimization; low-rank matrix recovery; matrix completion; sub-Nyquist sampling; sparse reconstruction; overcomplete dictionary; measurement matrix; basis pursuit; sparse approximation; approximate message passing; underdetermined linear systems; sparse signals; sparse optimization; low-rank matrix completion; multiple measurement vectors; support recovery; nuclear norm minimization; compressive imaging; structured sparsity
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Numeric address and provenance
Morton address: 36239938685390449 · morton4x16-v1
Coordinates: (3, 22, 2206, 10500). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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