[1]Department of Food Science, University of Copenhagen, Denmark
SPCA (Sparse PCA) estimates a PCA-like model with sparsity induced on scores or loadings via L1 norm bounding. Provides NIPASLS (deflation-based) and ASLS (simultaneous) algorithmic options with soft thresholding. The penalty parameter lambda drives selected loadings to zero, improving interpretability of high-dimensional models through automated variable selection.
References:
Rasmussen, M. A.; Bro, R. (2012). A tutorial on the Lasso approach to sparse modeling. Chemometrics and Intelligent Laboratory Systems, 119, 21-31. https://doi.org/10.1016/j.chemolab.2012.10.003
Witten, D. M.; Tibshirani, R.; Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 10(3), 515-534. https://doi.org/10.1093/biostatistics/kxp008
Zou, H.; Hastie, T.; Tibshirani, R. (2006). Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 15(2), 265-286. https://doi.org/10.1198/106186006X113430
Shen, H.; Huang, J. Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis, 99(6), 1015-1034. https://doi.org/10.1016/j.jmva.2007.06.007
