Bootstrap Confidence Regions for Learned Feature Embeddings
针对非矩阵数据（如图像或文本）通过算法学习得到的高维特征向量，研究了如何用Bootstrap方法构建其低维嵌入的置信区域，并通过模拟和空间蛋白质组学数据验证了方法的有效性。
Algorithmic feature learners provide high-dimensional vector representations for non-matrix structured data, like image or text collections. Low-dimensional projections derived from these representations, called embeddings, are often used to explore variation in these data. However, it is not clear how to assess the embedding uncertainty. We adapt methods developed for bootstrapping principal components analysis to the setting where features are algorithmically derived from nonmatrix data. We empirically compare the derived confidence areas in simulations, varying factors influencing feature learning and the bootstrap, like feature learning algorithm complexity and bootstrap sample size. We illustrate the proposed approaches on a spatial proteomics dataset, where we observe that embedding precision is not uniform across all tissue types. Code, data, and pretrained models are available as an R compendium in the supplementary materials. Supplementary files for this article are available online.