Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex
  Optimization   We propose a scalable method for semi-supervised (transductive) learning from
massive network-structured datasets. Our approach to semi-supervised learning
is based on representing the underlying hypothesis as a graph signal with small
total variation. Requiring a small total variation of the graph signal
representing the underlying hypothesis corresponds to the central smoothness
assumption that forms the basis for semi-supervised learning, i.e., input
points forming clusters have similar output values or labels. We formulate the
learning problem as a nonsmooth convex optimization problem which we solve by
appealing to Nesterovs optimal first-order method for nonsmooth optimization.
We also provide a message passing formulation of the learning method which
allows for a highly scalable implementation in big data frameworks.

18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys