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

2020/10/22 by Chia-Hao Liu, Christopher J. Wright, Liu, Chia-Hao +21 · 1 citation
Medicine · Neuroscience · #Advanced MRI Techniques and Applications #FOS: Computer and information sciences #FOS: Physical sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2010.11807

openalex publication_date 2020/10/22 · openalex created_date 2020/10/29 · openalex updated_date 2026/07/28

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.

Cited by

• A fast two-stage algorithm for non-negative matrix factorization in streaming data

Related

• nmfMapping: a cloud-based web application for non-negative matrix factorization of powder diffraction and pair distribution function datasets
• A fast two-stage algorithm for non-negative matrix factorization in streaming data
• Analysis of the robustness of NMF algorithms
• Normalizing Flows to Reconstruct Pseudo-PDFs
• Non--negative matrix factorization using the R package \textsfnnmf