用降维与正交约束提升噪声下动态系统临界突变检测精度
Noise-robust Contrastive Learning for Critical Transition Detection in Dynamical Systems
- 基于奇异值分解构建轻量神经网络，引入半正交约束训练
- 在高噪声数据中识别临界点准确率接近传统方法，但模型更轻量
- 适合处理含噪复杂时间序列的临界突变检测任务
在科学与工程领域，从复杂且含噪的时间序列数据中检测临界突变是一个基本挑战。这类突变可通过低维序参量的出现来预测，但其信号常被高幅值随机波动所掩盖。基于深度神经网络的标准对比学习方法虽有潜力，但通常参数过多且对无关噪声敏感，导致临界点识别不准确。为此，我们提出一种基于奇异值分解构建的神经网络架构，配合严格的半正交性约束训练算法，以增强传统对比学习性能。大量实验表明，该方法在识别临界突变方面达到与传统方法相当的性能，但模型显著更轻量，且对噪声具有更强的鲁棒性。
原文摘要 · Abstract (English)
Detecting critical transitions in complex, noisy time-series data is a fundamental challenge across science and engineering. Such transitions may be anticipated by the emergence of a low-dimensional order parameter, whose signature is often masked by high-amplitude stochastic variability. Standard contrastive learning approaches based on deep neural networks, while promising for detecting critical transitions, are often overparameterized and sensitive to irrelevant noise, leading to inaccurate identification of critical points. To address these limitations, we propose a neural network architecture, constructed using singular value decomposition technique, together with a strictly semi-orthogonality-constrained training algorithm, to enhance the performance of traditional contrastive learning. Extensive experiments demonstrate that the proposed method matches the performance of traditional contrastive learning techniques in identifying critical transitions, yet is considerably more lightweight and markedly more resistant to noise.
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