https://github.com/mamba413/eimpute
Efficiently Impute Large Scale Incomplete Matrix
https://github.com/mamba413/eimpute
Last synced: about 1 year ago
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Efficiently Impute Large Scale Incomplete Matrix
- Host: GitHub
- URL: https://github.com/mamba413/eimpute
- Owner: Mamba413
- License: other
- Created: 2020-03-11T12:34:23.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-01-16T16:20:21.000Z (over 2 years ago)
- Last Synced: 2025-01-22T09:52:23.006Z (over 1 year ago)
- Language: C++
- Homepage:
- Size: 4.09 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
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README
# eimpute: Efficiently IMPUTE Large Scale Incomplete Matrix
Introdution
----------
Matrix completion is a procedure for imputing the missing elements in matrices by using the information of observed elements. This procedure can be visualized as:
Matrix completion has attracted a lot of attention, it is widely applied in:
- tabular data imputation: recover the missing elements in data table;
- recommend system: estimate users' potantial preference for items pending purchased;
- image inpainting: inpaint the missing elements in digit images.
Software
----------
A computationally efficient R package, **eimpute** is developed for matrix completion.
### Installation
Install the stable version from CRAN:
```R
install.packages("eimpute")
```
### Advantage
In **eimpute**, matrix completion problem is solved by iteratively performing low-rank approximation and data calibration, which enjoy two admirable advantages:
- unbiased low-rank approximation for incomplete matrix
- less time consumption via truncated SVD
Moreover, **eimpute** also supports flexible data standardization.
Compare **eimpute** and **softimpute** in systhesis datasets $X_{m \times m}$ with $p$ proportion missing observations:
- $m$ is chosen as 1000, 2000, 3000, 4000
- $p$ is chosen as 0.1, 0.5, 0.9.
In high dimension case, als method in **softimpute** is a little faster than **eimpute** in low proportion of missing observations, as the proportion of missing observations increase, rsvd method in **eimpute** have a better performance than **softimpute** in time cost and test error. Compare with two method in **eimpute*, rsvd method is better than tsvd in time cost.
References
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- Rahul Mazumder, Trevor Hastie and Rob Tibshirani (2010) Spectra Regularization Algorithms for Learning Large Incomplete Matrices, Journal of Machine Learning Research 11 (2010) 2287-2322
- Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp (2011) Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Review Vol. 53, num. 2, pp. 217-288
Bug report
----------
Send an email to Zhe Gao at gaozh8@mail2.sysu.edu.cn