Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Recovery of Sparse Signals via $\ell_1$ Minimization   This article considers constrained $\ell_1$ minimization methods for the
recovery of high dimensional sparse signals in three settings: noiseless,
bounded error and Gaussian noise. A unified and elementary treatment is given
in these noise settings for two $\ell_1$ minimization methods: the Dantzig
selector and $\ell_1$ minimization with an $\ell_2$ constraint. The results of
this paper improve the existing results in the literature by weakening the
conditions and tightening the error bounds. The improvement on the conditions
shows that signals with larger support can be recovered accurately. This paper
also establishes connections between restricted isometry property and the
mutual incoherence property. Some results of Candes, Romberg and Tao (2006) and
Donoho, Elad, and Temlyakov (2006) are extended.

3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph