From the 1 of 27 linked papers with an AI index.
7 papers · 1 filter
Graph Distribution-valued Signals in Wasserstein Spaces: Theory and Applications
Yanan Zhao, Feng Ji, Xingchao Jian +1
We introduce a framework for graph signal processing (GSP) in which signals are represented as graph distribution-valued signals (GDSs), i.e., probability measures in a Wasserstein…
Optimal Sensor Placement via Graph-constrained Flow Matching
Feng Ji, Jingyang Dai, Wee Peng Tay +1
Optimal sensor placement is a fundamental problem in graph signal processing (GSP), where a limited number of sensors are deployed to reconstruct a continuous signal field. Existin…
Uncertainty Principle for Vertex-Time Graph Signal Processing
Yanan Zhao, Xingchao Jian, Feng Ji +2
We present an uncertainty principle for graph signals in the vertex-time domain, unifying the classical time-frequency and graph uncertainty principles within a single framework. B…
A Generalized Graph Signal Processing Framework for Multiple Hypothesis Testing over Networks
Xingchao Jian, Martin Gölz, Feng Ji +2
We consider the multiple hypothesis testing (MHT) problem over the joint domain formed by a graph and a measure space. On each sample point of this joint domain, we assign a hypoth…
A Graph Signal Processing Perspective of Network Multiple Hypothesis Testing with False Discovery Rate Control
Xingchao Jian, Martin Gölz, Feng Ji +2
We consider a multiple hypothesis testing problem in a sensor network over the joint spatio-temporal domain. The sensor network is modeled as a graph, with each vertex representing…
Generalized Graph Signal Reconstruction via the Uncertainty Principle
Yanan Zhao, Xingchao Jian, Feng Ji +2
We introduce a novel uncertainty principle for generalized graph signals that extends classical time-frequency and graph uncertainty principles into a unified framework. By definin…