https://github.com/cbica/sopnmf
SOPNMF is the fast python implementation of stochastic orthogonally projective non-negative matrix factorization
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: scholar.google, sciencedirect.com -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.4%) to scientific vocabulary
Last synced: 9 months ago
·
JSON representation
Repository
SOPNMF is the fast python implementation of stochastic orthogonally projective non-negative matrix factorization
Basic Info
- Host: GitHub
- Owner: CBICA
- License: mit
- Default Branch: master
- Homepage: http://www.junhaowen.com/SOPNMF/
- Size: 15.5 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of anbai106/SOPNMF
Created about 4 years ago
· Last pushed about 4 years ago
https://github.com/CBICA/SOPNMF/blob/master/
SOPNMFStochastic orthogonally projective non-negative matrix factorization
## About the project **SOPNMF** is the python implementation of the Matlab version of Orthogonal Projective Non-negative Matrix Factorization: [brainparts](https://github.com/asotiras/brainparts), and its stochastic extension. > :warning: **The documentation of this software is currently under development** ## Citing this work > Sotiras, A., Resnick, S.M. and Davatzikos, C., 2015. **Finding imaging patterns of structural covariance via non-negative matrix factorization**. Neuroimage, 108, pp.1-16. [doi:10.1016/j.neuroimage.2014.11.045](https://www.sciencedirect.com/science/article/pii/S1053811914009756?via%3Dihub) > TBD ## Publications around SOPNMF > Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G.B., Erus, G., Shou, H., Abdulkadir, A., Hwang, G., Dwyer, D.B. and Pigoni, A., 2022. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Medical Image Analysis, 75, p.102304. - [Link](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=4Wq_FukAAAAJ&sortby=pubdate&citation_for_view=4Wq_FukAAAAJ:9ZlFYXVOiuMC)
Owner
- Name: Center for Biomedical Image Computing & Analytics (CBICA)
- Login: CBICA
- Kind: organization
- Email: software@cbica.upenn.edu
- Location: Philadelphia, PA
- Website: https://www.med.upenn.edu/cbica/
- Twitter: CBICAannounce
- Repositories: 21
- Profile: https://github.com/CBICA
CBICA focuses on the development and application of advanced computation techniques.