Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse matrices for weighted sparse recovery   We derived the first sparse recovery guarantees for weighted $\ell_1$
minimization with sparse random matrices and the class of weighted sparse
signals, using a weighted versions of the null space property to derive these
guarantees. These sparse matrices from expender graphs can be applied very fast
and have other better computational complexities than their dense counterparts.
In addition we show that, using such sparse matrices, weighted sparse recovery
with weighted $\ell_1$ minimization leads to sample complexities that are
linear in the weighted sparsity of the signal and these sampling rates can be
smaller than those of standard sparse recovery. Moreover, these results reduce
to known results in standard sparse recovery and sparse recovery with prior
information and the results are supported by numerical experiments.

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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph