1 research outputs found
Graph Vertex Sampling with Arbitrary Graph Signal Hilbert Spaces
Graph vertex sampling set selection aims at selecting a set of ver-tices of a
graph such that the space of graph signals that can be reconstructed exactly
from those samples alone is maximal. In this context, we propose to extend
sampling set selection based on spectral proxies to arbitrary Hilbert spaces of
graph signals. Enabling arbitrary inner product of graph signals allows then to
better account for vertex importance on the graph for a sampling adapted to the
application. We first state how the change of inner product impacts sampling
set selection and reconstruction, and then apply it in the context of geometric
graphs to highlight how choosing an alternative inner product matrix can help
sampling set selection and reconstruction.Comment: Accepted at ICASSP 202