Validation of non-negative matrix factorization for assessment of atomic pair-distribution function (PDF) data in a real-time streaming context

We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data. We also present a newly developed software infrastructure for analyzing the PDF data arriving in streaming manner. We then apply two matrix factoriza...

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Bibliographic Details
Published in:arXiv.org 2020-10
Main Authors: Chia-Hao, Liu, Wright, Christopher J, Gu, Ran, Bandi, Sasaank, Wustrow, Allison, Todd, Paul K, O'Nolan, Daniel, Beauvais, Michelle L, Neilson, James R, Chupas, Peter J, Chapman, Karena W, Billinge, Simon J L
Format: Article
Language:English
Subjects:
Context
Distribution functions
Factorization
Principal components analysis
Online Access:Get full text
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Summary:We validate the use of matrix factorization for the automatic identification of relevant components from atomic pair distribution function (PDF) data. We also present a newly developed software infrastructure for analyzing the PDF data arriving in streaming manner. We then apply two matrix factorization techniques, Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF), to study simulated and experiment datasets in the context of in situ experiment.
ISSN:2331-8422