Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical
  Analysis and an Algorithm   Traditional machine learning algorithms assume that the training and test
data have the same distribution, while this assumption does not necessarily
hold in real applications. Domain adaptation methods take into account the
deviations in the data distribution. In this work, we study the problem of
domain adaptation on graphs. We consider a source graph and a target graph
constructed with samples drawn from data manifolds. We study the problem of
estimating the unknown class labels on the target graph using the label
information on the source graph and the similarity between the two graphs. We
particularly focus on a setting where the target label function is learnt such
that its spectrum is similar to that of the source label function. We first
propose a theoretical analysis of domain adaptation on graphs and present
performance bounds that characterize the target classification error in terms
of the properties of the graphs and the data manifolds. We show that the
classification performance improves as the topologies of the graphs get more
balanced, i.e., as the numbers of neighbors of different graph nodes become
more proportionate, and weak edges with small weights are avoided. Our results
also suggest that graph edges between too distant data samples should be
avoided for good generalization performance. We then propose a graph domain
adaptation algorithm inspired by our theoretical findings, which estimates the
label functions while learning the source and target graph topologies at the
same time. The joint graph learning and label estimation problem is formulated
through an objective function relying on our performance bounds, which is
minimized with an alternating optimization scheme. Experiments on synthetic and
real data sets suggest that the proposed method outperforms baseline
approaches.

11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci