Joint-sparse recovery from multiple measurements
Ewout van den Berg; Michael P. Friedlander · 2009 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
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Abstract (excerpt)
The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an extension of the single-measurement-vector (SMV) problem widely studied in compressed sensing. We analyze the recovery properties for two types of recovery algorithms. First, we show that recovery using sum-of-norm minimization cannot exceed the uniform recovery rate of sequential SMV using $\ell_1$ minimization, and that there are problems that can be solved with one approach but not with the other. Second, we analyze
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Metadata source: arXiv