SEA++: Multi-Graph-Based Higher-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

Yucheng Wang;Yuecong Xu;Jianfei Yang;Min Wu;Xiaoli Li;Lihua Xie;Zhenghua Chen
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Abstract

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between labeled source domains and unlabeled target domains. However, these methods face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically originates from multiple sensors, each with its unique distribution. This property poses difficulties in adapting existing UDA techniques, which mainly focus on aligning global features while overlooking the distribution discrepancies at the sensor level, thus limiting their effectiveness for MTS data. To address this issue, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to address domain discrepancy at both local and global sensor levels. At the local sensor level, we design endo-feature alignment, which aligns sensor features and their correlations across domains. To reduce domain discrepancy at the global sensor level, we design exo-feature alignment that enforces restrictions on global sensor features. We further extend SEA to SEA++ by enhancing the endo-feature alignment. Particularly, we incorporate multi-graph-based higher-order alignment for both sensor features and their correlations. Extensive empirical results have demonstrated the state-of-the-art performance of our SEA and SEA++ on six public MTS datasets for MTS-UDA.
SEA++:基于多图的高阶传感器对齐,用于多变量时间序列无监督领域适应。
无监督域自适应(UDA)方法通过最大限度地减少已标注源域和未标注目标域之间的域差异,成功地降低了标签依赖性。然而,这些方法在处理多变量时间序列(MTS)数据时面临挑战。MTS 数据通常来自多个传感器,每个传感器都有其独特的分布。现有的 UDA 技术主要关注全局特征的对齐,而忽略了传感器层面的分布差异,因此限制了其对 MTS 数据的有效性。为了解决这个问题,我们提出了一种实用的领域适配方案,即多变量时序无监督领域适配(MTS-UDA)。在本文中,我们为 MTS-UDA 提出了传感器对齐(SEA),旨在解决本地和全局传感器层面的领域差异问题。在本地传感器层面,我们设计了内部特征对齐(endo-feature alignment)技术,可对传感器特征及其跨域相关性进行对齐。为了减少全局传感器层面的领域差异,我们设计了外部特征对齐,对全局传感器特征实施限制。我们通过增强内部特征对齐,进一步将 SEA 扩展到 SEA++。特别是,我们为传感器特征及其相关性加入了基于多图的高阶对齐。广泛的实证结果表明,在 MTS-UDA 的六个公共 MTS 数据集上,我们的 SEA 和 SEA++ 具有最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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