Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model. Classic learning tools are often vulnerable to perturbations of the input covariates, and are sensitive to outliers and noise, and some tools are based on rigid algebraic assumptions. Distribution shifts are frequently occurring due to changes in raw materials for production, seasonality, a different user base, or even adversarial attacks. Therefore, there is a need for more effective distribution shift detection techniques. In this work, we propose a continual learning framework for monitoring and detecting distribution changes. We explore the problem in a latent space generated by a bio-inspired self-organizing clustering and statistical aspects of the latent space. In particular, we investigate the projections made by two topology-preserving maps: the Self-Organizing Map and the Scale Invariant Map. Our method can be applied in both a supervised and an unsupervised context. We construct the assessment of changes in the data distribution as a comparison of Gaussian signals, making the proposed method fast and robust. We compare it to other unsupervised techniques, specifically Principal Component Analysis (PCA) and Kernel-PCA. Our comparison involves conducting experiments using sequences of images (based on MNIST and injected shifts with adversarial samples), chemical sensor measurements, and the environmental variable related to ozone levels. The empirical study reveals the potential of the proposed approach.

翻译：对非平稳数据进行建模是持续学习领域中的一项挑战性问题，数据分布漂移可能导致机器学习模型性能下降。经典学习工具通常易受输入协变量扰动影响，且对异常值和噪声敏感，部分工具还基于刚性代数假设。由于生产原材料变化、季节性波动、用户群体差异乃至对抗攻击等因素，分布漂移频繁发生。因此，亟需更有效的分布漂移检测技术。本文提出一种持续学习框架，用于监测和检测数据分布变化。我们在由生物启发式自组织聚类生成的隐空间及其统计特性中探究该问题，特别研究了两种拓扑保持映射——自组织映射与尺度不变映射——的投影结果。该方法可同时适用于有监督与无监督场景。通过将数据分布变化的评估构建为高斯信号的比较分析，所提方法兼具快速性与鲁棒性。我们将其与主成分分析及核主成分分析等无监督技术进行比较，基于图像序列（基于MNIST数据及注入对抗样本的偏移数据）、化学传感器测量数据以及环境臭氧水平相关变量开展实验。实证研究表明了该方法的潜力。