Graph Filters for Signal Processing and Machine Learning on Graphs

arXiv:2211.08854v2 Announce Type: replace-cross
Abstract: Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, …

abstract arxiv convolutional neural networks crux cs.lg data domains eess.sp filters graph graphs image image data information machine machine learning machine learning techniques modern networks neural networks processing series signal time series type