opnmf
This package implements orthogonal projective non-negative matrix factorization as described in:
Z. Yang and E. Oja, "Linear and Nonlinear Projective Nonnegative Matrix Factorization," in IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 734-749, May 2010, doi: 10.1109/TNN.2010.2041361.
Citing
If you use this software, consider citing:
Sotiras A, Resnick SM, Davatzikos C. Finding imaging patterns of structural covariance via Non-Negative Matrix Factorization. Neuroimage. 2015;108:1-16. doi:10.1016/j.neuroimage.2014.11.045
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Metadata
1 maintainer
Release files for opnmf 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| opnmf-0.0.2.tar.gz | 2.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| opnmf-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.9 MB
Release files / opnmf-0.0.2.tar.gz
| Download URL | opnmf-0.0.2.tar.gz |
|---|---|
| Size | 2.9 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.5.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
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Release files / opnmf-0.0.2-py3-none-any.whl
| Download URL | opnmf-0.0.2-py3-none-any.whl |
|---|---|
| Size | 21.1 kB |
| Tags | Python 3 |
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| Uploaded via |
twine/3.5.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
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