dcTensor
Discrete Matrix/Tensor Decomposition
Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md https://github.com/rikenbit/dcTensor.
- Version1.3.0
- R versionunknown
- LicenseMIT
- Needs compilation?No
- Last release05/11/2024
Documentation
- Vignette1. Discretized Non-negative Matrix Factorization ('dNMF')
- Vignette2. Discretized Non-negative Tri-Matrix Factorization ('dNMTF')
- Vignette2. Discretized Singular Value Decomposition ('dSVD')
- Vignette3. Discretized Simultaneous Non-negative Matrix Factrozation ('dsiNMF')
- Vignette4. Discretized Joint Non-negative Matrix Factrozation ('djNMF')
- Vignette5. Discretized Partial Least Squares ('dPLS')
- Vignette6. Discretized Non-negative Tensor Factorization ('dNTF')
- Vignette7. Discretized Non-negative Tucker Decomposition ('dNTD')
- MaterialNEWS
Team
Koki Tsuyuzaki
Insights
Last 30 days
This package has been downloaded 252 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 12 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,460 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Jul 21, 2024 with 71 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports4 packages
- Suggests3 packages