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arXiv · 2010.11807

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

Chia-Hao Liu·Christopher J. Wright·Ran Gu·Sasaank Bandi·Allison Wustrow·Paul K. Todd·Daniel O'Nolan·Michelle L. Beauvais·James R. Neilson·Peter J. Chupas·Karena W. Chapman·Simon J. L. Billinge
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Abstract

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.

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Chia-Hao Liu, Christopher J. Wright, Ran Gu, Sasaank Bandi, Allison Wustrow, Paul K. Todd, Daniel O'Nolan, Michelle L. Beauvais, James R. Neilson, Peter J. Chupas, Karena W. Chapman, Simon J. L. Billinge. 2020-10-22. Validation of non-negative matrix factorization for assessment of atomic pair-distribution function (PDF) data in a real-time streaming context. https://arxiv.org/abs/2010.11807

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