Persistent homology analysis with nonnegative matrix factorization for 3D voxel data of iron ore sinters
Author:
Affiliation:
1. Center for Artificial Intelligence and Mathematical Data Science (Angels), Okayama University
2. Department of Materials Structure Science, School of High Energy Accelerator Science, SOKENDAI (The Graduate University for Advanced Studies)
Publisher
The Japan Society for Industrial and Applied Mathematics
Subject
General Engineering
Link
https://www.jstage.jst.go.jp/article/jsiaml/14/0/14_151/_pdf
Reference12 articles.
1. [1] I. Obayashi, T. Nakamura and Y. Hiraoka, Persistent homology analysis for materials research and persistent homology software: HomCloud, J. Phys. Soc. Jpn., 91 (2022), 091013.
2. [2] C. S. Pun, S. X. Lee and K. Xia, Persistent-homology-based machine learning: a survey and a comparative study, Artif. Intell. Rev., 55 (2022), 5169–5213.
3. [3] H. Adams et al., Persistence images: a stable vector representation of persistent homology, J. Mach. Learn. Res., 18(2017), 1–35.
4. [4] D. Lee and S. Seung, Learning the parts of objects by nonnegative matrix factorization, Nature, 401 (1999), 788–791.
5. [5] M. Kimura et al., Non-empirical identification of trigger sites in heterogeneous processes using persistent homology, Sci. Rep., 8 (2018), 3553.
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Finding “Trigger Sites” of Reactions Among Heterogeneous Materials from X-ray Microscopic Big Data Using Persistent Homology;The Minerals, Metals & Materials Series;2024
2. Detecting electrochemical changes in a nickel–zinc battery by operando x‐ray computed‐tomography analysis during charge–discharge tests;X-Ray Spectrometry;2023-09-07
3. Persistent homology analysis with nonnegative matrix factorization for 3D voxel data of iron ore sinters;JSIAM Letters;2022
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