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arXiv · 2301.02136

Multiscale Transforms for Signals on Simplicial Complexes

Naoki Saito·Stefan C. Schonsheck·Eugene Shvarts
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Abstract

Our previous multiscale graph basis dictionaries/graph signal transforms -- Generalized Haar-Walsh Transform (GHWT); Hierarchical Graph Laplacian Eigen Transform (HGLET); Natural Graph Wavelet Packets (NGWPs); and their relatives -- were developed for analyzing data recorded on nodes of a given graph. In this article, we propose their generalization for analyzing data recorded on edges, faces (i.e., triangles), or more generally $\kappa$-dimensional simplices of a simplicial complex (e.g., a triangle mesh of a manifold). The key idea is to use the Hodge Laplacians and their variants for hierarchical partitioning of a set of $\kappa$-dimensional simplices in a given simplicial complex, and then build localized basis functions on these partitioned subsets. We demonstrate their usefulness for data representation on both illustrative synthetic examples and real-world simplicial complexes generated from a co-authorship/citation dataset and an ocean current/flow dataset.

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Naoki Saito, Stefan C. Schonsheck, Eugene Shvarts. 2022-12-28. Multiscale Transforms for Signals on Simplicial Complexes. https://arxiv.org/abs/2301.02136

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