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InfoResearchPeer-reviewed
TSFA: A Two-Stage Feature Alignment Method for Unsupervised Open-Set Domain Adaptation in Time-Series Classification
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Summary
Researchers propose TSFA, a two-stage feature alignment method for unsupervised open-set domain adaptation in time-series classification. The method combines a time-frequency feature extractor with global alignment via similarity distribution entropy and local alignment via pseudolabel-guided self-supervised learning. Experiments on three real-world datasets show it outperforms state-of-the-art approaches.