The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

中文翻译：

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用于结构损伤检测的基于张量的在线学习模型

存储在张量中的多路数据的在线分析 已成为捕获底层结构和提取可用于学习预测模型的敏感特征的重要工具。然而，数据分布通常会随着时间而发展，当前的预测模型在未来可能无法充分代表。因此，在这种情况下需要增量更新基于张量的特征和模型系数。提出了一种新的基于张量的高效特征提取，命名为 Nesterov 随机梯度下降 (NeSGD)，用于在线 (CP) 分解。根据从 NeSGD 的结果矩阵中获得的新特征，触发在线预测模型更新过程的新标准。使用基于实验室和现实生活中的结构数据集的结构健康监测领域的实验评估表明，与现有的在线张量分析和模型学习相比，我们的方法提供了更准确的结果。结果表明，所提出的方法显着提高了分类错误率，能够同化正数据分布随时间的变化，并在所有案例研究中保持较高的预测准确性。