论文标题
学习功能嵌入的引导程序置信区域
Bootstrap Confidence Regions for Learned Feature Embeddings
论文作者
论文摘要
算法功能学习者为非矩阵结构化信号（例如图像，音频，文本和图形）提供了高维矢量表示。从这些表示形式得出的低维投影可用于探索这些数据集合之间的变化。但是，尚不清楚如何评估与这些预测相关的不确定性。我们将用于引导主组件分析开发的方法调整为从非矩阵数据中学到的功能的设置。我们从经验上比较了模拟中派生的置信区域，这些因素既影响特征学习和引导程序。在空间蛋白质组学数据上说明了方法。代码，数据和训练有素的模型作为R纲要发布。
Algorithmic feature learners provide high-dimensional vector representations for non-matrix structured signals, like images, audio, text, and graphs. Low-dimensional projections derived from these representations can be used to explore variation across collections of these data. However, it is not clear how to assess the uncertainty associated with these projections. We adapt methods developed for bootstrapping principal components analysis to the setting where features are learned from non-matrix data. We empirically compare the derived confidence regions in simulations, varying factors that influence both feature learning and the bootstrap. Approaches are illustrated on spatial proteomic data. Code, data, and trained models are released as an R compendium.
