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
Cluster labels are often used twice: first to discover groups, and then to test whether the discovered groups differ. This double use of the data makes ordinary two-sample $p$-values anti-conservative...
arXiv:2512. 05926v2 Announce Type: replace Abstract: We consider the fundamental problem of balanced $k$-means clustering.
By Wenyan Luo, Dustin G. Mixon
arXiv:2608. 14215v1 Announce Type: new Abstract: Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains.
By Johanna Hillebrand, Jan H\"ockendorff, J\"urgen Kusche, Kelin Luo, Heiko R\"oglin, Melanie Schmidt, Christian Sohler, Bernd Uebbing
arXiv:2509. 22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions.
By Sre\'cko {\DJ}ura\v{s}inovi\'c, Jean-Bernard Lasserre, Victor Magron
arXiv:2610.33177v1 Announce Type: new
Abstract: The Gaussian Mixture Model (abbrv. GMM) is a classic generative model for clustered data, for which spectral clustering based on dimension reduction vi...
By Xiaoyu Dong, Subhro Ghosh, Soumendu Sundar Mukherjee, Himasish Talukdar