2 research outputs found
Localization bounds for the graph translation
International audienceThe graph translation operator has been defined with good spectral properties in mind, and in particular with the end goal of being an isometric operator. Unfortunately, the resulting definitions do not provide good intuitions on a vertex-domain interpretation. In this paper, we show that this operator does have a vertex-domain interpretation as a diffusion operator using a polynomial approximation. We show that its impulse response exhibit an exponential decay of the energy way from the impulse, demonstrating localization preservation. Additionally, we formalize several techniques that can be used to study other graph signal operators
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE