Toward signal processing theory for graphs and non-Euclidean data

@article{Miller2010TowardSP,
  title={Toward signal processing theory for graphs and non-Euclidean data},
  author={Benjamin A. Miller and Nadya T. Bliss and Patrick J. Wolfe},
  journal={2010 IEEE International Conference on Acoustics, Speech and Signal Processing},
  year={2010},
  pages={5414-5417}
}
Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example… CONTINUE READING
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