用图原型框架提升稀疏标注下的出行方式识别准确率
ST-ProC: A Graph-Prototypical Framework for Robust Semi-Supervised Travel Mode Identification
- 构建图原型机制，利用数据流形和原型锚点捕捉轨迹特征
- 在真实稀疏标签场景下性能比顶尖方法高21.5%
- 适合交通分析、智慧城市等需要少标注的场景
从GPS轨迹进行出行方式识别（TMI）对城市智能至关重要，但标注成本高导致标签严重稀缺。现有半监督学习（SSL）方法因灾难性确认偏差且忽略数据内在流形而表现不佳。我们提出ST-ProC，一种新型图原型多目标半监督学习框架。该框架结合图原型核心与基础对比和师生一致性损失，通过图正则化、原型锚定及新颖的边界感知伪标签策略主动剔除噪声，有效利用数据流形。其核心由对比和师生一致性损失支持，确保高质量表征与稳定优化。在真实稀疏标签设置下，ST-ProC显著优于所有基线，性能较FixMatch等先进方法提升21.5%。
原文摘要 · Abstract (English)
Travel mode identification (TMI) from GPS trajectories is critical for urban intelligence, but is hampered by the high cost of annotation, leading to severe label scarcity. Prevailing semi-supervised learning (SSL) methods are ill-suited for this task, as they suffer from catastrophic confirmation bias and ignore the intrinsic data manifold. We propose ST-ProC, a novel graph-prototypical multi-objective SSL framework to address these limitations. Our framework synergizes a graph-prototypical core with foundational SSL Support. The core exploits the data manifold via graph regularization, prototypical anchoring, and a novel, margin-aware pseudo-labeling strategy to actively reject noise. This core is supported and stabilized by foundational contrastive and teacher-student consistency losses, ensuring high-quality representations and robust optimization. ST-ProC outperforms all baselines by a significant margin, demonstrating its efficacy in real-world sparse-label settings, with a performance boost of 21.5% over state-of-the-art methods like FixMatch.
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