REAMP: A Stochastic Resonance Approach for Multi-Change Point Detection in High-Dimensional Data

2026-01-12

AI Summary

This study addresses the challenge of detecting multiple change points in high-dimensional data, particularly when shifts involve higher-order moments or complex manifold structures. The authors propose a novel approach that integrates optimal transport with stochastic resonance: first, a two-stage dimensionality reduction yields a graph-count-based statistic; then, a stochastic resonance mechanism transforms multiple change points into global minima, enabling precise localization through a double-sharpening procedure. This work is the first to incorporate the physical principle of stochastic resonance into change point detection, overcoming the limitations of traditional local search strategies and achieving global awareness of multiple change points within intricate high-dimensional structures. The method significantly outperforms existing techniques in scenarios involving simultaneous shifts in both mean and variance, and demonstrates practical utility in time-lapse embryo monitoring by accurately identifying and visualizing cell division stages.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Detecting multiple structural breaks in high-dimensional data remains a challenge, particularly when changes occur in higher-order moments or within complex manifold structures. In this paper, we propose REAMP (Resonance-Enhanced Analysis of Multi-change Points), a novel framework that integrates optimal transport theory with the physical principles of stochastic resonance. By utilizing a two-stage dimension reduction via the Earth Movers Distance (EMD) and Shortest Hamiltonian Paths (SHP), we map high-dimensional observations onto a graph-based count statistic. To overcome the locality constraints of traditional search algorithms, we implement a stochastic resonance system that utilizes randomized Beta-density priors to vibrate the objective function. This process allows multiple change points to resonate as global minima across iterative simulations, generating a candidate point cloud. A double-sharpening procedure is then applied to these candidates to pinpoint precise change point locations. We establish the asymptotic consistency of the resonance estimator and demonstrate through simulations that REAMP outperforms state-of-the-art methods, especially in scenarios involving simultaneous mean and variance shifts. The practical utility of the method is further validated through an application to time-lapse embryo monitoring, where REAMP provides both accurate detection and intuitive visualization of cell division stages.

Problem

• multi-change point detection
• high-dimensional data
• structural breaks
• higher-order moments
• manifold structures

Innovation

• stochastic resonance
• optimal transport
• multi-change point detection
• high-dimensional data
• Earth Mover's Distance