Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressed Sensing of Block-Sparse Signals: Uncertainty Relations and
  Efficient Recovery   We consider compressed sensing of block-sparse signals, i.e., sparse signals
that have nonzero coefficients occurring in clusters. An uncertainty relation
for block-sparse signals is derived, based on a block-coherence measure, which
we introduce. We then show that a block-version of the orthogonal matching
pursuit algorithm recovers block $k$-sparse signals in no more than $k$ steps
if the block-coherence is sufficiently small. The same condition on
block-coherence is shown to guarantee successful recovery through a mixed
$\ell_2/\ell_1$-optimization approach. This complements previous recovery
results for the block-sparse case which relied on small block-restricted
isometry constants. The significance of the results presented in this paper
lies in the fact that making explicit use of block-sparsity can provably yield
better reconstruction properties than treating the signal as being sparse in
the conventional sense, thereby ignoring the additional structure in the
problem.

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The categories of this paper are: hep-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex