Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: $l_{2,p}$ Matrix Norm and Its Application in Feature Selection   Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as
computer vision, pattern recognition, biological study and etc. As an extension
of $l_1$ vector norm, the mixed $l_{2,1}$ matrix norm is often used to find
jointly sparse solutions. Moreover, an efficient iterative algorithm has been
designed to solve $l_{2,1}$-norm involved minimizations. Actually,
computational studies have showed that $l_p$-regularization ($0<p<1$) is
sparser than $l_1$-regularization, but the extension to matrix norm has been
seldom considered. This paper presents a definition of mixed $l_{2,p}$ $(p\in
(0, 1])$ matrix pseudo norm which is thought as both generalizations of $l_p$
vector norm to matrix and $l_{2,1}$-norm to nonconvex cases $(0<p<1)$.
Fortunately, an efficient unified algorithm is proposed to solve the induced
$l_{2,p}$-norm $(p\in (0, 1])$ optimization problems. The convergence can also
be uniformly demonstrated for all $p\in (0, 1]$. Typical $p\in (0,1]$ are
applied to select features in computational biology and the experimental
results show that some choices of $0<p<1$ do improve the sparse pattern of
using $p=1$.

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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con