Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Multiple Kernel Learning: Support Identification via Mirror
  Stratifiability   In statistical machine learning, kernel methods allow to consider infinite
dimensional feature spaces with a computational cost that only depends on the
number of observations. This is usually done by solving an optimization problem
depending on a data fit term and a suitable regularizer. In this paper we
consider feature maps which are the concatenation of a fixed, possibly large,
set of simpler feature maps. The penalty is a sparsity inducing one, promoting
solutions depending only on a small subset of the features. The group lasso
problem is a special case of this more general setting. We show that one of the
most popular optimization algorithms to solve the regularized objective
function, the forward-backward splitting method, allows to perform feature
selection in a stable manner. In particular, we prove that the set of relevant
features is identified by the algorithm after a finite number of iterations if
a suitable qualification condition holds. The main tools used in the proofs are
the notions of stratification and mirror stratifiability.

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The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio