One challenge impeding the analysis of terabyte scale X-ray scattering data from the Linac Coherent Light Source (LCLS) is determining the number of clusters required for the execution of traditional clustering algorithms. Here, we demonstrate that the previous work using bi-cross validation to determine the number of singular vectors directly maps to the spectral clustering problem of estimating both the number of clusters and hyperparameter values. Applying this method to LCLS X-ray scattering data enables the identification of dropped shots without manually setting boundaries on detector fluence and provides a path toward identifying rare and anomalous events.

中文翻译：

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谱聚类超参数的双向验证

阻碍分析来自直线加速器相干光源 (LCLS) 的 TB 级 X 射线散射数据的挑战是确定执行传统聚类算法所需的聚类数量。在这里，我们证明了之前使用双向验证来确定奇异向量数量的工作直接映射到估计聚类数量和超参数值的谱聚类问题。将此方法应用于 LCLS X 射线散射数据，无需手动设置检测器注量边界即可识别掉落的镜头，并提供了识别罕见和异常事件的途径。