1 research outputs found
Graph Filters for Signal Processing and Machine Learning on Graphs
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, we provide a comprehensive overview of graph
filters, including the different filtering categories, design strategies for
each type, and trade-offs between different types of graph filters. We discuss
how to extend graph filters into filter banks and graph neural networks to
enhance the representational power; that is, to model a broader variety of
signal classes, data patterns, and relationships. We also showcase the
fundamental role of graph filters in signal processing and machine learning
applications. Our aim is that this article provides a unifying framework for
both beginner and experienced researchers, as well as a common understanding
that promotes collaborations at the intersections of signal processing, machine
learning, and application domains