Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Graphs from Smooth and Graph-Stationary Signals with Hidden
  Variables   Network-topology inference from (vertex) signal observations is a prominent
problem across data-science and engineering disciplines. Most existing schemes
assume that observations from all nodes are available, but in many practical
environments, only a subset of nodes is accessible. A natural (and sometimes
effective) approach is to disregard the role of unobserved nodes, but this
ignores latent network effects, deteriorating the quality of the estimated
graph. Differently, this paper investigates the problem of inferring the
topology of a network from nodal observations while taking into account the
presence of hidden (latent) variables. Our schemes assume the number of
observed nodes is considerably larger than the number of hidden variables and
build on recent graph signal processing models to relate the signals and the
underlying graph. Specifically, we go beyond classical correlation and partial
correlation approaches and assume that the signals are smooth and/or stationary
in the sought graph. The assumptions are codified into different constrained
optimization problems, with the presence of hidden variables being explicitly
taken into account. Since the resulting problems are ill-conditioned and
non-convex, the block matrix structure of the proposed formulations is
leveraged and suitable convex-regularized relaxations are presented. Numerical
experiments over synthetic and real-world datasets showcase the performance of
the developed methods and compare them with existing alternatives.

30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci