arXiv Machine Learning June 11, 2026 By Juliette Murris, Bernadette Stolz, Karsten Borgwardt

From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features

Read the original on arXiv Machine Learning →

arXiv:2606. 11911v1 Announce Type: cross Abstract: Persistence diagrams are common representations in topological data analysis, but they do not naturally live in a vector space, and the statistical tools developed for comparing them have largely evolved separately from those used for downstream prediction.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Stochastic Dynamics on Persistence Diagram Space via Reinforcement Learning

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reinforcement-learning

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Hugging Face Trending Papers

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Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams

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.

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