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 is from work done by:
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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.
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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 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 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.
From R:
R> devtools::install_gitlab("xpca/xpcar/xpcaR")
Locally from command line:
$ R CMD INSTALL xpcaR