Natural methods of unsupervised topological alignment

Mikhail S. Arbatskiy; Maksim V. Kukushkin; Dmitriy E. Balandin; Alexey V. Churov

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arXiv · 2510.26631

Natural methods of unsupervised topological alignment

Mikhail S. Arbatskiy·Maksim V. Kukushkin·Dmitriy E. Balandin·Alexey V. Churov

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Abstract

In this paper, we consider methods for the diagonal multi-omics integration of heterogeneous datasets. Several approaches to the nature of biological heterogeneity are analyzed and developed to comprehend more clearly the generated differences. Specifically, the extremal trace problems for the coupled Laplacian on sets homeomorphic to the Stiefel manifold embedded in the complex Euclidean space are investigated. The gradient ascent method for the maximization problem is elaborated in the classical terms of functional analysis, which is of significant interest in itself. On this basis, we introduce a novel characteristic of dataset heterogeneity by employing the norm of the difference between the maximum and minimum points.

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BibTeX RIS

Mikhail S. Arbatskiy, Maksim V. Kukushkin, Dmitriy E. Balandin, Alexey V. Churov. 2026-09-21. Natural methods of unsupervised topological alignment. https://arxiv.org/abs/2510.26631

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