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Bi-cross validation of spectral clustering hyperparameters

Published online by Cambridge University Press:  24 April 2020

Sioan Zohar Open the ORCID record for Sioan Zohar [Opens in a new window]  and
Chun Hong Yoon
Sioan Zohar*
Affiliation:
Photon Data and Controls Systems, Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, California94025, USA
Chun Hong Yoon
Affiliation:
Photon Data and Controls Systems, Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, California94025, USA
*
a)Author to whom correspondence should be addressed. Electronic mail: [email protected]
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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.

Keywords

XRDspectroscopymachine learningin situ diffraction
Type
Proceedings Paper
Information
Powder Diffraction , Volume 35 , Issue 2 , June 2020 , pp. 112 - 116
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Copyright
Copyright © International Centre for Diffraction Data 2020

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