Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data driven estimation of Laplace-Beltrami operator   Approximations of Laplace-Beltrami operators on manifolds through graph
Lapla-cians have become popular tools in data analysis and machine learning.
These discretized operators usually depend on bandwidth parameters whose tuning
remains a theoretical and practical problem. In this paper, we address this
problem for the unnormalized graph Laplacian by establishing an oracle
inequality that opens the door to a well-founded data-driven procedure for the
bandwidth selection. Our approach relies on recent results by Lacour and
Massart [LM15] on the so-called Lepski's method.

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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-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: econ
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg