Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction and estimation consistency of sparse multi-class penalized
  optimal scoring   Sparse linear discriminant analysis via penalized optimal scoring is a
successful tool for classification in high-dimensional settings. While the
variable selection consistency of sparse optimal scoring has been established,
the corresponding prediction and estimation consistency results have been
lacking. We bridge this gap by providing probabilistic bounds on out-of-sample
prediction error and estimation error of multi-class penalized optimal scoring
allowing for diverging number of classes.

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The categories of this paper are: cs
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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: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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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: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an