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Maximum diversity and weighting for invariants of periodi...
[Submitted on 14 Sep 2025 (v1), last revised 12 Jul 2026 (this v · 2025-09-14 · via stat updates on arXiv.org

Statistics > Machine Learning

arXiv:2509.11146 (stat)

[Submitted on 14 Sep 2025 (v1), last revised 12 Jul 2026 (this version, v2)]

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Abstract:Magnitude, obtained as a special case of Euler characteristic of enriched category, represents a sense of the size of metric spaces and is related to classical notions such as cardinality, dimension, and volume. While the studies have explained the meaning of magnitude from various perspectives, continuity also gives a valuable view of magnitude. Based on established results about continuity of magnitude and maximum diversity, this article focuses on continuity of weighting, a distribution whose totality is magnitude, and its variation corresponding to maximum diversity. Meanwhile, recent studies also illuminated the connection between magnitude and data analysis by applying magnitude theory to point clouds representing the data or the set of model parameters. This article will also provide an application for time series analysis by introducing a new kind of invariants of periodic time series, where the invariance follows directly from the continuity results. As a use-case, a simple machine learning experiment is conducted with real-world data, in which the suggested invariants improved the performance.

Submission history

From: Byungchang So [view email]
[v1] Sun, 14 Sep 2025 07:33:05 UTC (486 KB)
[v2] Sun, 12 Jul 2026 09:59:33 UTC (511 KB)

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