3 papers indexed
Purui Zhang, Feng Ji, Yanan Zhao, Bihan Wen, Wee Peng Tay
Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate am…
Yanan Zhao, Feng Ji, Xingchao Jian, Wee Peng Tay
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 space. This perspective addresses fundamental limitations of classical vector-based GSP, including…
Feng Ji, Jingyang Dai, Wee Peng Tay, Sirajudeen Gulam Razul
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. Existing GSP methods rely on combinatorial optimization over discretized graphs, resulting in high computat…