Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric Basis Pursuit via Sparse Kernel-based Learning   Signal processing tasks as fundamental as sampling, reconstruction, minimum
mean-square error interpolation and prediction can be viewed under the prism of
reproducing kernel Hilbert spaces. Endowing this vantage point with
contemporary advances in sparsity-aware modeling and processing, promotes the
nonparametric basis pursuit advocated in this paper as the overarching
framework for the confluence of kernel-based learning (KBL) approaches
leveraging sparse linear regression, nuclear-norm regularization, and
dictionary learning. The novel sparse KBL toolbox goes beyond translating
sparse parametric approaches to their nonparametric counterparts, to
incorporate new possibilities such as multi-kernel selection and matrix
smoothing. The impact of sparse KBL to signal processing applications is
illustrated through test cases from cognitive radio sensing, microarray data
imputation, and network traffic prediction.

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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
37
The categories of this paper are: supr-con