https://github.com/sandialabs/xpcar
Implementation of XPCA in R (https://arxiv.org/abs/1808.07510)
https://github.com/sandialabs/xpcar
scr-2407 snl-comp-science-libs snl-data-analysis
Last synced: about 1 year ago
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Implementation of XPCA in R (https://arxiv.org/abs/1808.07510)
- Host: GitHub
- URL: https://github.com/sandialabs/xpcar
- Owner: sandialabs
- License: other
- Created: 2023-06-26T22:31:03.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-06-26T22:31:16.000Z (about 3 years ago)
- Last Synced: 2025-06-13T10:09:44.154Z (about 1 year ago)
- Topics: scr-2407, snl-comp-science-libs, snl-data-analysis
- Language: R
- Homepage:
- Size: 52.7 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
## XPCA
## Table of Contents
* [Background](#background)
* [Install](#install)
## Background
XPCA factors an observed data matrix X (m rows x n columns) into two factors, A (m x k) and B (n x k) where rank = k. Each row in X corresponds to an observed element, and each column corresponds to a feature of the observed element. These columns can be of mixed variable types - continuous, count, binary, etc.
This is the R version of the library. The most recent version of the library is
implemented in python.
xpcaR implements 3 capabilities: xpca, pca, and coca.
### coca
`coca` is from work done by:
1. Fang Han and Han Liu. Semiparametric principal component analysis. In
NIPS’12: Proceedings of the 26th Annual Conference on Neural Information Pro- cessing Systems, pages 171–179, 2012.
URL http://papers.nips.cc/paper/4809-semiparametric-principal-component-analysis.
2. Bernhard Egger, Dinu Kaufmann, Sandro Sch ̈onborn, Volker Roth, and Thomas Vetter. Copula eigenfaces — semiparametric principal component analysis for facial appearance modeling.
In VISIGRAPP’16: Proceedings of the 11th Joint Conference on Computer Vi- sion, Imaging and Computer Graphics Theory and Applications, pages 50–58. SciTePress, 2016. 10.5220/0005718800480056.
### xpca
`xpca` is Sandia-built work done by Cliff Anderson-Bergman, Tamara Kolda, and Kina Kincher-Winoto.
Paper is in review and available on arXiv:
C. Anderson-Bergman, T. G. Kolda, K. Kincher-Winoto. XPCA: Extending PCA for a Combination of Discrete and Continuous Variables. arXiv:1808.07510, 2018.
### pca
`pca` is the well-known algorithm. For reference:
Michael E. Tipping and Christopher M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611– 622, August 1999. 10.1111/1467-9868.00196.
## Install
From R:
```
R> devtools::install_gitlab("xpca/xpcar/xpcaR")
```
Locally from command line:
```
$ R CMD INSTALL xpcaR
```