Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature selection guided by structural information   In generalized linear regression problems with an abundant number of
features, lasso-type regularization which imposes an $\ell^1$-constraint on the
regression coefficients has become a widely established technique. Deficiencies
of the lasso in certain scenarios, notably strongly correlated design, were
unmasked when Zou and Hastie [J. Roy. Statist. Soc. Ser. B 67 (2005) 301--320]
introduced the elastic net. In this paper we propose to extend the elastic net
by admitting general nonnegative quadratic constraints as a second form of
regularization. The generalized ridge-type constraint will typically make use
of the known association structure of features, for example, by using temporal-
or spatial closeness. We study properties of the resulting "structured elastic
net" regression estimation procedure, including basic asymptotics and the issue
of model selection consistency. In this vein, we provide an analog to the
so-called "irrepresentable condition" which holds for the lasso. Moreover, we
outline algorithmic solutions for the structured elastic net within the
generalized linear model family. The rationale and the performance of our
approach is illustrated by means of simulated and real world data, with a focus
on signal regression.

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The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: econ
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
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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: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph