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Assuming equal covariance Digits - MissForest.ipynb
Assuming equal covariance Digits - MissForest.ipynb
 
 
Assuming equal covariance NUCLEAR NORM - MISS FOREST.ipynb
Assuming equal covariance NUCLEAR NORM - MISS FOREST.ipynb
 
 
Not assume equal cov DPER - MICE-SOFT IMPUTE.ipynb
Not assume equal cov DPER - MICE-SOFT IMPUTE.ipynb
 
 
Not assume equal cov Digits Dper- mice - soft impute.ipynb
Not assume equal cov Digits Dper- mice - soft impute.ipynb
 
 
Not assume equal cov Digits RESI.ipynb
Not assume equal cov Digits RESI.ipynb
 
 
Not assume equal cov MNIST DPER - SOFT IMPUTE.ipynb
Not assume equal cov MNIST DPER - SOFT IMPUTE.ipynb
 
 
Not assume equal cov MNIST RESI.ipynb
Not assume equal cov MNIST RESI.ipynb
 
 
Not assuming equal covariance Digits - MissForest.ipynb
Not assuming equal covariance Digits - MissForest.ipynb
 
 
Not assuming equal covariance NUCLEAR NORM - MISS FOREST.ipynb
Not assuming equal covariance NUCLEAR NORM - MISS FOREST.ipynb
 
 
README.md
README.md
 
 
RESI-KNNI without equal covariance assumption.ipynb
RESI-KNNI without equal covariance assumption.ipynb
 
 
Simulation Assuming equal covariance matrix .ipynb
Simulation Assuming equal covariance matrix .ipynb
 
 
Simulation not assume equal covariance matrix .ipynb
Simulation not assume equal covariance matrix .ipynb
 
 
assume equal cov FASHION_MNIST - DPER - SOFT IMPUTE.ipynb
assume equal cov FASHION_MNIST - DPER - SOFT IMPUTE.ipynb
 
 
assume equal covariance MNIST - DPER-SOFT-IMPUTE.ipynb
assume equal covariance MNIST - DPER-SOFT-IMPUTE.ipynb
 
 
assuming equal covariance Digits RESI (1).ipynb
assuming equal covariance Digits RESI (1).ipynb
 
 
assuming equal covariance MNIST RESI (1).ipynb
assuming equal covariance MNIST RESI (1).ipynb
 
 
assuming equal covariance RESI (1).ipynb
assuming equal covariance RESI (1).ipynb
 
 
assuming equal covariance matrix DPER - MICE-SOFT IMPUTE.ipynb
assuming equal covariance matrix DPER - MICE-SOFT IMPUTE.ipynb
 
 
assuming equal covariance matrix Digits DPER - MICE-SOFT IMPUTE.ipynb
assuming equal covariance matrix Digits DPER - MICE-SOFT IMPUTE.ipynb
 
 
not assume equal_cov__FASHION_MNIST - DPER - SOFT IMPUTE.ipynb
not assume equal_cov__FASHION_MNIST - DPER - SOFT IMPUTE.ipynb
 
 

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DPER: Efficient Parameter Estimation for Randomly Missing Data

This repo contains scripts to reproduce the results in the paper:

"DPER: Efficient Parameter Estimation for Randomly Missing Data", published in Knowledge-based Systems

Usage

The notebooks are created by Google's Colaboratory and can be run online with Colab

The contents of each notebook is describe as in its title: whether there is the equal covariance matrix assumption + algorithm name(s) + dataset name for big datasets (there is no dataset name the notebook that implements on small datasets).

References

We recommend you to cite our following paper when using these codes for further investigation:

@article{nguyen2021dper,
  title={DPER: Efficient Parameter Estimation for Randomly Missing Data},
  author={Nguyen, Thu and Nguyen-Duy, Khoi Minh and Nguyen, Duy Ho Minh and Nguyen, Binh T and Wade, Bruce Alan},
  journal={arXiv preprint arXiv:2106.05190},
  year={2021}
}

Further requests can directly be sent to the corresponding authors: Thu Nguyen ([email protected]) and Binh T. Nguyen ([email protected]) for an appropriate permission.

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