arXiv Statistics ML By Dustin G. Mixon, Tom Needham, Nathan Willey

Certifiably Correct Fr\'echet Means in Orbit Spaces

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The paper investigates Fréchet means in orbit spaces arising from finite orthogonal group actions, a problem known to be NP-hard. It introduces spectral and semidefinite relaxations inspired by group synchronization, and provides a semidefinite certificate that can certify global optimality even when pairwise alignment fails. The authors demonstrate the effectiveness of their methods on Gaussian data with sign ambiguity and through numerical experiments on districting plan ensembles and time‑series clustering.

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arXiv Machine Learning
Oct 28

The Clustering Occupation Kernel (CLOCK) Method for Time Series Arising from Dynamical Systems

The paper introduces the Clustering Occupation Kernel (CLOCK) method, a representation-based framework that embeds time series from dynamical systems into a reproducing kernel Hilbert space using a time‑averaged occupation kernel. This embedding allows trajectory comparison via the RKHS norm and, for differential equation systems, approximates the kernel mean embedding of the invariant Sinai‑Ruelle‑Bowen measure, effectively measuring the maximum mean discrepancy between attractors. CLOCK handles unequal‑length and irregularly‑spaced data, incorporates Takens’ embedding for general time series, and demonstrates superior performance over benchmark methods on synthetic data and real intracranial EEG recordings, with ablation studies confirming theoretical convergence and noise robustness.

By Michael L. Wells, Kamel Lahouel, Victor Rielly, Bruno Jedynak