Two-Stage Alignments Framework for Unsupervised Domain Adaptation on Time Series Data

Abstract

Unsupervised Domain Adaptation (UDA) aims to free models from labeled information of target domain by minimizing the discrepancy of distributions between different domains. Most existing methods are designed to learn domain-invariant features either by domain discrimination or by matching lower-order moments. However, these methods are not robust due to the limited representation of statistical characteristics for non-Gaussian distributions and thus fail in domain matching. In addition, they often focus on matching distributions while not considering class decision boundaries between domains. To address these issues, we propose a novel Two-Stage Alignments Framework (TSAF) for UAD, which not only performs arbitrary-order moment matching to approximately characterize complex non-Gaussian distributions, but also utilizes domain-specific decision boundaries to align the probabilistic outputs of classifiers. Moreover, the reconstruction-based task is introduced to enhance the representation of the inherent characteristics for specific distribution. Extensive experiments on three real-world time series datasets demonstrate that: 1) our model evidently outperforms many state-of-the-art domain adaptation methods in cross-domain classification tasks; 2) TSAF can learn domain-invariant features efficiently.