Bi-cross validation of spectral clustering hyperparameters. Issue 2 (June 2020)

• Record Type: Journal Article
• Authors: Zohar, Sioan; Yoon, Chun Hong
• Is Part Of: Powder diffraction. Volume 35:Issue 2(2020)
• Page Start: 112
• Page End: 116
• Publication Date: 2020-06
• Subjects: XRD, -- spectroscopy, -- machine learning, -- in situ diffraction; Powder metallurgy -- Periodicals; Materials -- Analysis -- Periodicals; 620.1127
• DOI: 10.1017/S0885715620000214
• Languages: English
• ISSNs: 0885-7156

Abstract

Abstract : 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.