pcaL1-package: pcaL1: L1-Norm PCA Methods

In pcaL1: L1-Norm PCA Methods

pcaL1: L1-Norm PCA Methods

Description

This package contains implementations of six principal component analysis methods using the L1 norm. The package depends on COIN-OR Clp version >= 1.17.4. The methods implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and Kanade 2003, 2005), L1-PCA* (Brooks, Dula, and Boone 2013), L1-PCAhp (Visentin, Prestwich and Armagan 2016), wPCA (Park and Klabjan 2016), and awPCA (Park and Klabjan 2016).

Details

Package:	pcaL1
Version:	1.5.7
Date:	2023-01-16
License:	GPL (>=3)
URL:	http://www.optimization-online.org/DB_HTML/2012/04/3436.html, http://www.coin-or.org
SystemRequirements:	COIN-OR Clp (>= 1.17.4)

Index:

awl1pca                 awPCA 
l1pca                   L1-PCA
l1pcahp                 L1-PCAhp
l1pcastar               L1-PCA*
l1projection            L1-Norm Projection on a Subspace
L2PCA_approx            Subroutine for awl1pca
l2projection            L2-Norm Projection on a Subspace
pcal1                   PCA-L1
pcalp                   PCA-Lp
pcaL1-package           pcaL1: L1-Norm PCA Methods
plot.awl1pca            Plot an awl1pca Object
plot.l1pca              Plot an l1pca Object
plot.l1pcahp            Plot an l1pcahp Object
plot.l1pcastar          Plot an l1pcastar Object
plot.pcal1              Plot a pcal1 Object
plot.pcalp              Plot a pcalp Object
plot.wl1pca             Plot an wl1pca Object
plot.sharpel1pca        Plot a sharpel1pca Object
sharpel1pca             SharpeEL1-PCA
sharpel1rs              SharpEl1-RS
sparsel1pca             SparseEl1-PCA
wl1pca                  wPCA 

Author(s)

Sapan Jot <[e-mail]>, Paul Brooks <[e-mail]>, Andrea Visentin <[e-mail]>,Young Woong Park <[e-mail]>, and Yi-Hui Zhou <[e-mail]>

Maintainer: Paul Brooks <[e-mail]>

References

1. Brooks and Dula (2017) Estimating L1-Norm Best-Fit Lines, submitted

2. Brooks J.P., Dula J.H., and Boone E.L. (2013) A Pure L1-Norm Princpal Component Analysis, Computational Statistics & Data Analysis, 61:83-98. DOI:10.1016/j.csda.2012.11.007

3. Ke Q. and Kanade T. (2005) Robust L1 Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming, IEEE Conference on Computer Vision and Pattern Recognition. DOI:10.1109/CVPR.2005.309

4. Kwak N. (2008) Principal Component Analysis Based on L1-Norm Maximization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30: 1672-1680. DOI:10.1109/TPAMI.2008.114

5. Kwak N. (2014) Principal Component Analysis by Lp-Norm Maximization, IEEE Transactions on Cybernetics, 44:594-609. DOI:10.1109/TCYB.2013.2262936

6. Park, Y.W. and Klabjan, D. (2016) Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis, IEEE International Conference on Data Mining (ICDM). DOI: 10.1109/ICDM.2016.0054

7. Visentin A., Prestwich S., and Armagan S. T. (2016) Robust Principal Component Analysis by Reverse Iterative Linear Programming, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 593-605. DOI:10.1007/978-3-319-46227-1_37

8. Zhou, Y.-H. and Marron, J.S. (2016) Visualization of Robust L1PCA, Stat, 5:173-184. DOI:10.1002/sta4.113

pcaL1 documentation built on Jan. 22, 2023, 1:55 a.m.