The content discusses the challenges of processing signals on graph edges in Graph Signal Processing (GSP) due to techniques being defined only on nodes. The proposed LGLMS algorithm transforms edge signals onto node representations using the Line Graph, enabling efficient online prediction of time-varying edge signals under Gaussian noise and missing observations. By utilizing adaptive algorithms and well-developed GSP tools, LGLMS demonstrates effectiveness in various applications such as traffic flow and meteorological data prediction.
The methodology section explains how time-varying function values are represented on nodes and transformed into spectral domains for processing. The Line Graph transformation is utilized to project edge signals onto nodes, allowing for effective signal estimation using a bandlimited filter. Experimental results show that LGLMS outperforms baselines in predicting temperature and wind speed data accurately, showcasing its scalability potential for larger datasets.
Overall, the study highlights the significance of applying adaptive GSP algorithms to graph edges, emphasizing the efficiency and effectiveness of the LGLMS algorithm in processing time-varying edge signals with noisy and missing observations.
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by Yi Yan,Ercan... lúc arxiv.org 02-29-2024
https://arxiv.org/pdf/2311.00656.pdfYêu cầu sâu hơn