References
- Bien, J., Xu, Y., Mahoney, M. W. 2010. CUR from a sparse optimization viewpoint. In Advances in Neural Information Processing Systems (pp. 217-225).
- Cadima, J., Jolliffe, I. T. 1995. Loading and correlations in the interpretation of principle components. Journal of Applied Statistics, 22(2): 203-214.10.1080/757584614
- Croux, C., Ruiz-Gazen, A. 2005. High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1): 206-226.10.1016/j.jmva.2004.08.002
- Croux, C., Filzmoser, P., Oliveira, M. R. 2007. Algorithms for Projection–Pursuit robust principal component analysis. Chemometrics and Intelligent Laboratory Systems, 87(2): 218-225.10.1016/j.chemolab.2007.01.004
- Croux, C., Filzmoser, P., Fritz, H. 2013. Robust sparse principal component analysis. Technometrics, 55(2): 202-214.10.1080/00401706.2012.727746
- D’Aspremont, A., El Ghaoui, L., Jordan, M. I., Lanckriet, G. R. 2007. A direct formulation for sparse PCA using semidefinite programming. SIAM review, 49(3): 434-448.10.1137/050645506
- Guo, J., James, G., Levina, E., Michailidis, G., Zhu, J. 2010. Principal component analysis with sparse fused loadings. Journal of Computational and Graphical Statistics, 19(4): 930-946.10.1198/jcgs.2010.08127439490725878487
- He, Y., Zhou, W., Qian, G., Chen, B. 2014. Methane storage in metal–organic frameworks. Chemical Society Reviews, 43(16): 5657-5678.10.1039/C4CS00032C24658531
- Hubert, M., Rousseeuw, P. J., Van Aelst, S. 2008. High-breakdown robust multivariate methods. Statistical Science, 92-119.
- Hubert, M., Reynkens, T., Schmitt, E., Verdonck, T. 2016. Sparse PCA for high-dimensional data with outliers. Technometrics, 58(4): 424-434.10.1080/00401706.2015.1093962
- Jenatton, R., Obozinski, G., Bach, F. 2010, March. Structured sparse principal component analysis. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (pp. 366-373).
- Jolliffe, I. T. 1995. Rotation of principal components: choice of normalization constraints. Journal of Applied Statistics, 22(1): 29-35.10.1080/757584395
- Jolliffe, I. T., Trendafilov, N. T., Uddin, M. 2003. A modified principal component technique based on the LASSO. Journal of computational and Graphical Statistics, 12(3): 531-547.10.1198/1061860032148
- Journée M., Nesterov Y., Richtárik P., Sepulchre R. 2010 Generalized power method for sparse principal component analysis. J Mach Learn Res 11:517–553.
- Li, G., Chen, Z. 1985. Projection-pursuit approach to robust dispersion matrices and principal components: primary theory and Monte Carlo. Journal of the American Statistical Association, 80(391): 759-766.10.1080/01621459.1985.10478181
- Locantore, N., Marron, J. S., Simpson, D. G., Tripoli, N., Zhang, J. T., Cohen, K. L., ..., Fan, J. 1999. Robust principal component analysis for functional data. Test, 8(1): 1-73.10.1007/BF02595862
- R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Rousseeuw, P. J., Croux, C. 1993. Alternatives to the median absolute deviation. Journal of the American Statistical association, 88(424): 1273-1283.10.1080/01621459.1993.10476408
- Rousseeuw, P. J., Driessen, K. V. 1999. A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3): 212-223.10.1080/00401706.1999.10485670
- Sigg, C. D., Buhmann, J. M. 2008, July. Expectation-maximization for sparse and non-negative PCA. In Proceedings of the 25th international conference on Machine learning, ACM, pp. 960-967.
- Wang, H., Li, B., Leng, C. 2009. Shrinkage tuning parameter selection with a diverging number of parameters. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(3): 671-683.10.1111/j.1467-9868.2008.00693.x
- Zou, H., Hastie, T., Tibshirani, R. 2006. Sparse principal component analysis. Journal of computational and graphical statistics, 15(2): 265-286.10.1198/106186006X113430