Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric Bayesian label prediction on a large graph using truncated
  Laplacian regularization   This article describes an implementation of a nonparametric Bayesian approach
to solving binary classification problems on graphs. We consider a hierarchical
Bayesian approach with a prior that is constructed by truncating a series
expansion of the soft label function using the graph Laplacian eigenfunctions
as basis functions. We compare our truncated prior to the untruncated Laplacian
based prior in simulated and real data examples to illustrate the improved
scalability in terms of size of the underlying graph.

2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys