从纠缠到对齐：面向无监督时间序列域自适应的表示空间分解
From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation
AI总结：
提出DARSD框架，通过表示空间分解解缠可迁移知识，结合对抗不变基、原型伪标签和混合对比优化，在四个基准上超越12种UDA算法。
AI中文摘要：
域偏移对时间序列分析构成了根本性挑战，在源域上训练的模型应用于具有不同但相似分布的目标域时，往往会出现严重失败。虽然当前的無監督域自适应（UDA）方法试图对齐跨域特征分布，但它们通常将特征视为不可分割的整体，忽略了支配域自适应的内在组成。我们提出了DARSD，一个具有理论可解释性的新型UDA框架，从表示空间分解的角度显式实现UDA任务。我们的核心见解是，有效的域自适应不仅需要对齐，还需要对混合表示中的可迁移知识进行原则性的解缠。DARSD由三个协同组件组成：（I）一个对抗性可学习的公共不变基，将原始特征投影到域不变子空间中，同时保留语义内容；（II）一种原型伪标签机制，基于置信度动态分离目标特征，阻止错误累积；（III）一种混合对比优化策略，同时增强特征聚类和一致性，同时缓解新出现的分布差距。在四个基准（WISDM、HAR、HHAR和MFD）上进行的全面实验表明，DARSD相对于12种UDA算法具有优越性，在53个场景中的35个中达到最优性能，并在所有基准中排名第一。
英文摘要：
Domain shift poses a fundamental challenge in time series analysis, where models trained on source domain often fail dramatically when applied in target domain with different yet similar distributions. While current unsupervised domain adaptation (UDA) methods attempt to align cross-domain feature distributions, they typically treat features as indivisible entities, ignoring their intrinsic compositions that govern domain adaptation. We introduce DARSD, a novel UDA framework with theoretical explainability that explicitly realizes UDA tasks from the perspective of representation space decomposition. Our core insight is that effective domain adaptation requires not just alignment, but principled disentanglement of transferable knowledge from mixed representations. DARSD consists of three synergistic components: (I) An adversarial learnable common invariant basis that projects original features into a domain-invariant subspace while preserving semantic content; (II) A prototypical pseudo-labeling mechanism that dynamically separates target features based on confidence, hindering error accumulation; (III) A hybrid contrastive optimization strategy that simultaneously enforces feature clustering and consistency while mitigating emerging distribution gaps. Comprehensive experiments conducted on four benchmarks (WISDM, HAR, HHAR, and MFD) demonstrate DARSD's superiority against 12 UDA algorithms, achieving optimal performance in 35 out of 53 scenarios and ranking first across all benchmarks.
