Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Learning from Filtered Signals: Graph System and Diffusion Kernel
  Identification   This paper introduces a novel graph signal processing framework for building
graph-based models from classes of filtered signals. In our framework,
graph-based modeling is formulated as a graph system identification problem,
where the goal is to learn a weighted graph (a graph Laplacian matrix) and a
graph-based filter (a function of graph Laplacian matrices). In order to solve
the proposed problem, an algorithm is developed to jointly identify a graph and
a graph-based filter (GBF) from multiple signal/data observations. Our
algorithm is valid under the assumption that GBFs are one-to-one functions. The
proposed approach can be applied to learn diffusion (heat) kernels, which are
popular in various fields for modeling diffusion processes. In addition, for
specific choices of graph-based filters, the proposed problem reduces to a
graph Laplacian estimation problem. Our experimental results demonstrate that
the proposed algorithm outperforms the current state-of-the-art methods. We
also implement our framework on a real climate dataset for modeling of
temperature signals.

11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
2
The categories of this paper are: math