Paper Info
Title
Kernel-Based Reconstruction Of Space-Time Functions Via Extended Graphs
Abstract
Signals evolving over graphs emerge naturally in a number of applications related to network science. A frequently encountered challenge pertains to reconstructing such signals given their values on subsets of vertices at possibly different time instants. Spatiotemporal dynamics can be leveraged so that a small number of vertices suffices to achieve accurate reconstruction. The present paper broadens the existing kernelbased graph-function reconstruction framework to handle timeevolving functions over (possibly dynamic) graphs. The proposed approach introduces the novel notion of graph extension to enable kernel-based estimators over time and space. Numerical tests with real data corroborate that judiciously capturing time-space dynamics markedly improves reconstruction performance.
Year
Venue
Keywords
2016
2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS
Graph signal reconstruction, graph extension, kernel ridge regression, space-time kernels
Field
DocType
ISSN
Graph kernel,Space time,Network science,Kernel (linear algebra),Mathematical optimization,Vertex (geometry),Computer science,Kernel embedding of distributions,Symmetric matrix,Estimator
Conference
1058-6393
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Vassilis N. Ioannidis1147.34
Daniel Romero26612.80
Georgios B. Giannakis34977340.58