从动物携带的运动时间序列中发现通用行为的半监督流程
A Semi-Supervised Pipeline for Generalized Behavior Discovery from Animal-Borne Motion Time Series
AI总结：
本文提出了一种半监督流程，通过KDE+HDR包含分数在有限标注和严重类别不平衡下发现生态运动时间序列中的通用行为。
摘要：
从动物携带的传感器中学习行为分类具有挑战性，因为标签稀缺，类别高度不平衡，且行为可能不在标注集中。我们研究了从海鸥的短多元运动片段中发现通用行为，其中每个样本都是包含3轴IMU加速度（20Hz）和GPS速度的序列，涵盖九个专家标注的行为类别。我们提出了一种半监督发现流程，该流程包括（i）从标注子集学习嵌入函数，（ii）在标注和未标注样本的嵌入上执行标签引导聚类以形成候选行为组，（iii）通过包含分数决定发现的组是否真正新颖。我们的关键贡献是KDE + HDR（最高密度区域）包含分数，该分数衡量发现的聚类分布在已知类别分布中包含或包含的程度；最佳匹配包含分数作为可解释的新颖性统计量。在实验中，当整个行为被排除在监督之外并仅出现在未标注池中时，该方法恢复了一个独特的聚类，包含分数通过低重叠标志新颖性，而在无新颖行为的负对照设置中，重叠始终更高。这些结果表明，基于HDR的包含性提供了在有限标注和严重类别不平衡的情况下，对生态运动时间序列中通用类发现的实用且定量的测试。
英文摘要：
Learning behavioral taxonomies from animal-borne sensors is challenging because labels are scarce, classes are highly imbalanced, and behaviors may be absent from the annotated set. We study generalized behavior discovery in short multivariate motion snippets from gulls, where each sample is a sequence with 3-axis IMU acceleration (20 Hz) and GPS speed, spanning nine expert-annotated behavior categories. We propose a semi-supervised discovery pipeline that (i) learns an embedding function from the labeled subset, (ii) performs label-guided clustering over embeddings of both labeled and unlabeled samples to form candidate behavior groups, and (iii) decides whether a discovered group is truly novel using a containment score. Our key contribution is a KDE + HDR (highest-density region) containment score that measures how much a discovered cluster distribution is contained within, or contains, each known-class distribution; the best-match containment score serves as an interpretable novelty statistic. In experiments where an entire behavior is withheld from supervision and appears only in the unlabeled pool, the method recovers a distinct cluster and the containment score flags novelty via low overlap, while a negative-control setting with no novel behavior yields consistently higher overlaps. These results suggest that HDR-based containment provides a practical, quantitative test for generalized class discovery in ecological motion time series under limited annotation and severe class imbalance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
