Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Recovery of Fusion Frame Structured Signals   Fusion frames are collection of subspaces which provide a redundant
representation of signal spaces. They generalize classical frames by replacing
frame vectors with frame subspaces. This paper considers the sparse recovery of
a signal from a fusion frame. We use a block sparsity model for fusion frames
and then show that sparse signals under this model can be compressively sampled
and reconstructed in ways similar to standard Compressed Sensing (CS). In
particular we invoke a mixed l1/l2 norm minimization in order to reconstruct
sparse signals. In our work, we show that assuming a certain incoherence
property of the subspaces and the apriori knowledge of it allows us to improve
recovery when compared to the usual block sparsity case.

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The categories of this paper are: econ
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin