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Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation

Paper recorded by Signals 4 on 2026-09-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-01 on arXiv · recorded by Signals 4 on 2026-09-02

Category: cs.LG · 机器学习 · first seen 2026-09-02

Abstract

This paper introduces the Sierpiński-Knopp (SK) Wasserstein distance, a fast metric between persistence diagrams. The SK-Wasserstein distance, denoted $d_{\mathrm{SK}}$, maps diagram points and their diagonal projections to the unit interval via the Sierpiński-Knopp space-filling curve on the upper diagonal triangle. The encoded point sets are then efficiently matched via one-dimensional optimal a

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#161 most recent of 215 cs.LG papers we have recorded · ↑ newer: Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagn · ↓ older: Optimizing Byzantine Node Placement in Decentralized Federated Learnin
Cite this page: Sierpiński--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation: the #161 most recent of 215 cs.LG papers we have recorded (as of 2026-09-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/sierpi-ski-knopp-wasserstein-distance-for-persistence-diagrams-and-applications-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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