用无界分层对比聚类，从日常影像中自动识别个人环境类型并关联健康数据。
Infinite hierarchical contrastive clustering for personal digital envirotyping
- 基于对比学习与棒棒糖先验，实现无需预设数量的无限层级聚类。
- 在真实个体数据上成功分离出具有特征差异的个人环境，并形成有意义的类别结构。
- 适合研究环境与健康关系的心理学、公共卫生及可穿戴计算领域学者。
日常环境对健康与行为有深远影响。近期研究表明，通过生态瞬时评估（EMA）获取的日常环境图像，结合计算机视觉技术进行数字环境表征（digital envirotyping），可揭示环境特征与健康结果之间的关联。为在个体层面系统研究此类效应，需将图像分组为个体日常生活中遇到的不同环境；这些环境可进一步聚类为具有相似特征的关联环境类型，并与健康结果关联分析。本文提出无限层级对比聚类（infinite hierarchical contrastive clustering）以解决该问题。在已有对比聚类框架基础上，本方法 a) 通过在预测簇概率上施加棒棒糖先验（stick-breaking prior），无需完整狄利克雷过程即可支持任意数量的簇；b) 引入参与者特定的预测损失，促使同一簇内不同环境形成清晰的子簇。实验表明，该模型能有效识别出具有区分性的个人环境，并将其组织为有意义的环境类型。我们进一步展示了所得聚类如何与多种健康结果相关联，凸显该方法推动环境表征范式发展的潜力。
原文摘要 · Abstract (English)
Daily environments have profound influence on our health and behavior. Recent work has shown that digital envirotyping, where computer vision is applied to images of daily environments taken during ecological momentary assessment (EMA), can be used to identify meaningful relationships between environmental features and health outcomes of interest. To systematically study such effects on an individual level, it is helpful to group images into distinct environments encountered in an individual's daily life; these may then be analyzed, further grouped into related environments with similar features, and linked to health outcomes. Here we introduce infinite hierarchical contrastive clustering to address this challenge. Building on the established contrastive clustering framework, our method a) allows an arbitrary number of clusters without requiring the full Dirichlet Process machinery by placing a stick-breaking prior on predicted cluster probabilities; and b) encourages distinct environments to form well-defined sub-clusters within each cluster of related environments by incorporating a participant-specific prediction loss. Our experiments show that our model effectively identifies distinct personal environments and groups these environments into meaningful environment types. We then illustrate how the resulting clusters can be linked to various health outcomes, highlighting the potential of our approach to advance the envirotyping paradigm.
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