Stretched non-negative matrix factorization. Gu, R., Rakita, Y., Lan, L., Thatcher, Z., Kamm, G. E., O’Nolan, D., Mcbride, B., Wustrow, A., Neilson, J. R., Chapman, K. W., Du, Q., & Billinge, S. J. L. npj Computational Materials, 10(1):1–15, August, 2024. Publisher: Nature Publishing Group![Stretched non-negative matrix factorization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.
@article{gu_stretched_2024,
title = {Stretched non-negative matrix factorization},
volume = {10},
copyright = {2024 The Author(s)},
issn = {2057-3960},
url = {https://www.nature.com/articles/s41524-024-01377-5},
doi = {10.1038/s41524-024-01377-5},
abstract = {A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.},
language = {en},
number = {1},
urldate = {2024-10-10},
journal = {npj Computational Materials},
author = {Gu, Ran and Rakita, Yevgeny and Lan, Ling and Thatcher, Zach and Kamm, Gabrielle E. and O’Nolan, Daniel and Mcbride, Brennan and Wustrow, Allison and Neilson, James R. and Chapman, Karena W. and Du, Qiang and Billinge, Simon J. L.},
month = aug,
year = {2024},
note = {Publisher: Nature Publishing Group},
keywords = {Theory and computation, Computational methods},
pages = {1--15},
file = {Full Text PDF:/Users/yevgenyr/Zotero/storage/UUG7CH3U/Gu et al. - 2024 - Stretched non-negative matrix factorization.pdf:application/pdf},
}
Downloads: 0
{"_id":"56CjCkn8WbujbTbCy","bibbaseid":"gu-rakita-lan-thatcher-kamm-onolan-mcbride-wustrow-etal-stretchednonnegativematrixfactorization-2024","author_short":["Gu, R.","Rakita, Y.","Lan, L.","Thatcher, Z.","Kamm, G. E.","O’Nolan, D.","Mcbride, B.","Wustrow, A.","Neilson, J. R.","Chapman, K. W.","Du, Q.","Billinge, S. J. L."],"bibdata":{"bibtype":"article","type":"article","title":"Stretched non-negative matrix factorization","volume":"10","copyright":"2024 The Author(s)","issn":"2057-3960","url":"https://www.nature.com/articles/s41524-024-01377-5","doi":"10.1038/s41524-024-01377-5","abstract":"A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.","language":"en","number":"1","urldate":"2024-10-10","journal":"npj Computational Materials","author":[{"propositions":[],"lastnames":["Gu"],"firstnames":["Ran"],"suffixes":[]},{"propositions":[],"lastnames":["Rakita"],"firstnames":["Yevgeny"],"suffixes":[]},{"propositions":[],"lastnames":["Lan"],"firstnames":["Ling"],"suffixes":[]},{"propositions":[],"lastnames":["Thatcher"],"firstnames":["Zach"],"suffixes":[]},{"propositions":[],"lastnames":["Kamm"],"firstnames":["Gabrielle","E."],"suffixes":[]},{"propositions":[],"lastnames":["O’Nolan"],"firstnames":["Daniel"],"suffixes":[]},{"propositions":[],"lastnames":["Mcbride"],"firstnames":["Brennan"],"suffixes":[]},{"propositions":[],"lastnames":["Wustrow"],"firstnames":["Allison"],"suffixes":[]},{"propositions":[],"lastnames":["Neilson"],"firstnames":["James","R."],"suffixes":[]},{"propositions":[],"lastnames":["Chapman"],"firstnames":["Karena","W."],"suffixes":[]},{"propositions":[],"lastnames":["Du"],"firstnames":["Qiang"],"suffixes":[]},{"propositions":[],"lastnames":["Billinge"],"firstnames":["Simon","J.","L."],"suffixes":[]}],"month":"August","year":"2024","note":"Publisher: Nature Publishing Group","keywords":"Theory and computation, Computational methods","pages":"1–15","file":"Full Text PDF:/Users/yevgenyr/Zotero/storage/UUG7CH3U/Gu et al. - 2024 - Stretched non-negative matrix factorization.pdf:application/pdf","bibtex":"@article{gu_stretched_2024,\n\ttitle = {Stretched non-negative matrix factorization},\n\tvolume = {10},\n\tcopyright = {2024 The Author(s)},\n\tissn = {2057-3960},\n\turl = {https://www.nature.com/articles/s41524-024-01377-5},\n\tdoi = {10.1038/s41524-024-01377-5},\n\tabstract = {A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-10-10},\n\tjournal = {npj Computational Materials},\n\tauthor = {Gu, Ran and Rakita, Yevgeny and Lan, Ling and Thatcher, Zach and Kamm, Gabrielle E. and O’Nolan, Daniel and Mcbride, Brennan and Wustrow, Allison and Neilson, James R. and Chapman, Karena W. and Du, Qiang and Billinge, Simon J. L.},\n\tmonth = aug,\n\tyear = {2024},\n\tnote = {Publisher: Nature Publishing Group},\n\tkeywords = {Theory and computation, Computational methods},\n\tpages = {1--15},\n\tfile = {Full Text PDF:/Users/yevgenyr/Zotero/storage/UUG7CH3U/Gu et al. - 2024 - Stretched non-negative matrix factorization.pdf:application/pdf},\n}\n\n","author_short":["Gu, R.","Rakita, Y.","Lan, L.","Thatcher, Z.","Kamm, G. E.","O’Nolan, D.","Mcbride, B.","Wustrow, A.","Neilson, J. R.","Chapman, K. W.","Du, Q.","Billinge, S. J. L."],"key":"gu_stretched_2024","id":"gu_stretched_2024","bibbaseid":"gu-rakita-lan-thatcher-kamm-onolan-mcbride-wustrow-etal-stretchednonnegativematrixfactorization-2024","role":"author","urls":{"Paper":"https://www.nature.com/articles/s41524-024-01377-5"},"keyword":["Theory and computation","Computational methods"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/network/files/qLT8is5AxWgBiXbed","dataSources":["4xHt7KTqQrLWSerBJ"],"keywords":["theory and computation","computational methods"],"search_terms":["stretched","non","negative","matrix","factorization","gu","rakita","lan","thatcher","kamm","o’nolan","mcbride","wustrow","neilson","chapman","du","billinge"],"title":"Stretched non-negative matrix factorization","year":2024}