no code implementations • 7 May 2024 • Changran Peng, Yi Yan, Ercan E. Kuruoglu
Efficient and robust prediction of graph signals is challenging when the signals are under impulsive noise and have missing data.
no code implementations • 7 May 2024 • Changran Peng, Yi Yan, Ercan E. Kuruoglu
In the presence of impulsive noise, and missing observations, accurate online prediction of time-varying graph signals poses a crucial challenge in numerous application domains.
no code implementations • 7 May 2024 • Yi Yan, Ercan E. Kuruoglu
The usage of the Hodge Laplacian on a binary-sign forward propagation enables Bi-SCNN to efficiently and effectively represent simplicial features that have higher-order structures than traditional graph node representations.
no code implementations • 27 Jan 2024 • Yi Yan, Changran Peng, Ercan Engin Kuruoglu
The LMS-GNN is a combination of adaptive graph filters and Graph Neural Networks (GNN).
no code implementations • 1 Nov 2023 • Yi Yan, Ercan Engin Kuruoglu
The processing of signals on graph edges is challenging considering that Graph Signal Processing techniques are defined only on the graph nodes.
no code implementations • 1 Mar 2022 • Yi Yan, Radwa Adel, Ercan Engin Kuruoglu
In this paper, we introduce an adaptive graph normalized least mean pth power (GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP techniques, including bandlimited filtering and node sampling, to estimate sampled graph signals under impulsive noise.
no code implementations • 15 Jan 2022 • Yi Yan, Ercan E. Kuruoglu, Mustafa A. Altınkaya
Recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity.