Signal Processing on Cell Complexes
This foundational work provides a general approach for processing signals on complex structures like graphs and meshes, impacting machine learning and signal processing.
The paper introduces signal processing on regular cell complexes as a unifying framework for non-Euclidean domains, enabling the derivation of Hodge Laplacians to construct convolutional filters for linear and non-linear filtering.
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.