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We validate the use of matrix factorization for the automatic identificationof relevant components from atomic pair distribution function (PDF) data. Wealso present a newly developed software infrastructure for analyzing the PDFdata arriving in streaming manner. We then apply two matrix factorizationtechniques, Principal Component Analysis (PCA) and Non-negative MatrixFactorization (NMF), to study simulated and experiment datasets in the contextof in situ experiment.

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