Might integrate prior knowledge into error calculation will help to generate feature embedding. I am newbie to matrix decomposition, could you kindly suggest how to code it with R or python?
Wang Lu
These codes might help:
1. https://github.com/awslabs/dgl-ke/blob/master/docs/source/kg.rst
2. https://github.com/smarini/MaDDA
3. https://github.com/smarini/MaDDA
4. https://github.com/linxihui/NNLM
5. https://github.com/eclipse/deeplearning4j-examples/issues/1003
6. https://github.com/JuliaMatrices/LowRankApprox.jl
7. https://github.com/zhanglabtools/DSMO.course
8. https://github.com/cdt15/lingam/blob/master/lingam/direct_lingam.py
9. https://github.com/welch-lab/liger
In addition, these papers might be an asset, have a look:
1. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.3635&rep=rep1&type=pdf
2. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3312-5
3. https://www.sciencedirect.com/science/article/pii/S0168952518301240
4. https://arxiv.org/pdf/1712.03337.pdf
I thought was the point of Bayesian statistics. See the attachment below 👇 David Booth