Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms   Sparse Principal Component Analysis (PCA) is a prevalent tool across a
plethora of subfields of applied statistics. While several results have
characterized the recovery error of the principal eigenvectors, these are
typically in spectral or Frobenius norms. In this paper, we provide entrywise
$\ell_{2,\infty}$ bounds for Sparse PCA under a general high-dimensional
subgaussian design. In particular, our results hold for any algorithm that
selects the correct support with high probability, those that are sparsistent.
Our bound improves upon known results by providing a finer characterization of
the estimation error, and our proof uses techniques recently developed for
entrywise subspace perturbation theory.

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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
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The categories of this paper are: eess
8
The categories of this paper are: hep-ex
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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: chem-ph
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat