ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022 · 31 citations · 32 references
ComputingComputer ScienceComputational GeometryComputer VisionMachine LearningDeep LearningDigital Signal ProcessingGraph-based LearningNeural NetworksSignal ProcessingConvolutional Neural NetworksEuclidean DomainsFilteringFiltersGraph LaplacianGraph Signal ProcessingLinear FilteringSimplicial Complexes
The processing of signals supported on non-Euclidean domains has attracted large interest recently. Thus far, such non-Euclidean domains have been abstracted primarily as graphs with signals supported on the nodes, though the processing of signals on more general structures such as simplicial complexes has also been considered. In this paper, we give an introduction to signal processing on (abstract) regular cell complexes, which provide a unifying framework encompassing graphs, simplicial complexes, cubical complexes and various meshes as special cases. We discuss how appropriate Hodge Laplacians for these cell complexes can be derived. These Hodge Laplacians enable the construction of convolutional filters, which can be employed in linear filtering and non-linear filtering via neural networks defined on cell complexes.
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Backpropagation Applied to Handwritten Zip Code Recognition
Yann LeCun, Bernhard E. Boser, J. S. Denker et al. · Neural Computation · 1989 · 11.9K citations
David I Shuman, Sunil K. Narang, Pascal Frossard et al. · IEEE Signal Processing Magazine · 2013 · 4.4K citations · Full text