Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams

Abstract:Persistence Diagram (PD) is known to capture point cloud topology effectively, but its computation has high time complexity. Expected Persistence Diagram (EPD) has been developed to reduce the time cost by studying the topology of multiple subsets of a point cloud and it serves as a distribution of topological features. Existing EPD vectorizations often rely on predefined point transformations, such as Gaussian or landscape functions. We study an alternative discretization based on Voronoi histograms, which trades smooth functional approximation for adaptive partition-based counting. We propose to use Voronoi Diagram-based histogram as the vectorization of EPD, without imposing an explicit smooth point transformation model. Under stated separation and normalization conditions, we establish stability bounds and characterize when the histogram representation preserves Wasserstein-scale variation. We demonstrate the effectiveness of our proposed representation on real-world datasets which have significant topological features for classification and dimensionality reduction tasks.

Machine Learning (cs.LG)

arXiv:2607.27126 [cs.LG]

(or arXiv:2607.27126v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.27126 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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