References
- Alcala, C. F., and Joe Qin, S. (2011), “Analysis and Generalization of Fault Diagnosis Methods for Process Monitoring,” Journal of Process Control, 21, 322–330.
- Arteaga, F., and Ferrer, A. (2002), “Dealing With Missing Data in mspc: Several Methods, Different Interpretations, Some Examples,” Journal of Chemometrics, 16, 408–418.
- ——— (2005), “Framework for Regression-Based Missing Data Imputation Methods in On-Line mspc,” Journal of Chemometrics, 19, 439–447.
- Bach, F. (2007), “Consistency of the Group Lasso and Multiple Kernel Learning,” Journal of Machine Learning Research, 9, 1179–1225.
- Browne, M. W. (2001), “An Overview of Analytic Rotation in Exploratory Factor Analysis,” Multivariate Behavioral Research, 36, 111–150.
- Camacho, J. (2010), “Missing-Data Theory in the Context of Exploratory Data Analysis,” Chemometrics and Intelligent Laboratory Systems, 103, 8–18.
- ——— (2011), “Observation-Based Missing Data Methods for Exploratory Data Analysis to Unveil the Connection Between Observations and Variables in Latent Subspace Models,” Journal of Chemometrics, 25, 592–600.
- Camacho, J., Maciá-Fernández, G., Díaz-Verdejo, J., and García-Teodoro, P. (2014), “Tackling the Big Data 4 vs for Anomaly Detection,” Proceedings—IEEE INFOCOM, 500–505.
- Camacho, J., Padilla, P., Daz-Verdejo, J., Smith, K., and Lovett, D. (2011), “Least-Squares Approximation of a Space Distribution for a Given Covariance and Latent Sub-Space,” Chemometrics and Intelligent Laboratory Systems, 105, 171–180.
- Camacho, J., Pérez-Villegas, A., García-Teodoro, P., and Maciá-Fernández, G. (2016), “PCA-Based Multivariate Statistical Network Monitoring for Anomaly Detection,” Computers and Security, 59, 118--137.
- Camacho, J., Pérez-Villegas, A., Rodríguez-Gómez, R. A., and nas, E. J.-M. (2015), “Multivariate Exploratory Data Analysis (meda) Toolbox for Matlab,” Chemometrics and Intelligent Laboratory Systems, 143, 49–57.
- Cliff, N. (1966), “Orthogonal Rotation to Congruence,” Psychometrika, 31, 33–42.
- Costello, A., and Osborne, J. (2005), “Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis,” Practical Assessment, Research & Evaluation, 10, 1– 9.
- Crawford, C. B., and Ferguson, G. A. (1970), “A General Rotation Criterion and Its Use in Orthogonal Rotation,” Psychometrika, 35, 321– 332.
- de Juan, A., and Tauler, R. (2006), “Multivariate Curve Resolution (MCR) From 2000: Progress in Concepts and Applications,” Critical Reviews in Analytical Chemistry, 36, 163–176.
- Fabrigar, L., Wegener, D., MacCallum, R., and Strahan, E. (1999), “Evaluating the Use of Exploratory Factor Analysis in Psychological Research,” Psychological Methods, 4, 272–299.
- Han, J., and Kamber, M. (2006), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA: Elsevier.
- Jackson, J. (2003), A User’s Guide to Principal Components, England: Wiley-Interscience.
- Jacob, L., Obozinski, G., and Vert, J.-P. (2009), “Group Lasso With Overlaps and Graph Lasso,” in Proceedings of the 26th International Conference on Machine Learning, Montreal, Canada.
- Jenatton, R., Audibert, J.-Y., and Bach, F. (2009), “Structured Variable Selection With Sparsity-Inducing Norms,” Journal of Machine Learning Research, 12, 2777–2824.
- Jenatton, R., Obozinski, G., and Bach, F. (2009), “Structured Sparse Principal Component Analysis,” in Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) (Vol. 9), pp. 366–373.
- Jolliffe, I. (1995), “Rotation of Principal Components: Choice of Normalization Constraints,” Journal of Applied Statistics, 22, 29–35.
- ——— (2002), Principal Component Analysis, EEUU: Springer Verlag Inc.
- Jolliffe, I., Trendafilov, N., and Uddin, M. (2003), “A Modified Principal Component Technique Based on the LASSO,” Journal of Computational and Graphical Statistics, 12, 531--547.
- Kaiser, H. F. (1958), “The Varimax Criterion for Analytic Rotation in Factor Analysis,” Psychometrika, 23, 187–200.
- Lin, C.-J. (2007), “Projected Gradient Methods for Nonnegative Matrix Factorization,” Neural Computation, 19, 2756–2779.
- Mackey, L. (2008), “Deflation Methods for Sparse PCA,” in NIPS, pp. 1–8.
- Nelson, P., Taylor, P., and MacGregor, J. (1996), “Missing Data Methods in PCA and PLS: Score Calculations With Incomplete Observations,” Chemometrics and Intelligent Laboratory Systems, 35, 45–65.
- Nomikos, P., and MacGregor, J. (1995), “Multivariate SPC Charts for Monitoring Batch Processes,” Technometrics, 37, 41–59.
- Rasmussen, M. A., and Bro, R. (2012), “A Tutorial on the Lasso Approach to Sparse Modeling,” Chemometrics and Intelligent Laboratory Systems, 119, 21–31.
- Saccenti, E., and Camacho, J. (2015), “On the Use of the Observation-Wise k-Fold Operation in PCA Cross-Validation,” Journal of Chemometrics, 29, 467–478.
- Saccenti, E., Smilde, A. K., Westerhuis, J. A., and Hendriks, M. M. W. B. (2011a), “Tracy-Widom Statistic for the Largest Eigenvalue of Autoscaled Real Matrices,” Journal of Chemometrics, 25, 644–652.
- Saccenti, E., Westerhuis, J. A., Smilde, A. K., van der Werf, M. J., Hageman, J. A., and Hendriks, M. M. W. B. (2011b), “Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data,” PLoS ONE, 6, e20747.
- Sjöstrand, K., and Clemmensen, L. (2012), “Spasm: A Matlab Toolbox for Sparse Statistical Modeling,” Journal of Statistical Software. Available http://www2.imm.dtu.dk/projects/spasm/references/spasm.pdf.
- Tibshirani, R. (1994), “Regression Selection and Shrinkage via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
- Timmerman, M. E., Kiers, H. A., and Smilde, A. K. (2007), “Estimating Confidence Intervals for Principal Component Loadings: A Comparison Between the Bootstrap and Asymptotic Results,” British Journal of Mathematical and Statistical Psychology, 60, 295–314.
- Warth, B., Parich, A., Bueschl, C., Schoefbeck, D., Neumann, N. K. N., Kluger, B., Schuster, K., Krska, R., Adam, G., Lemmens, M.et al., (2014), “GC–MS Based Targeted Metabolic Profiling Identifies Changes in the Wheat Metabolome Following Deoxynivalenol Treatment,” Metabolomics, 11, 722–738.
- Wold, S. (1978), “Cross-Validatory Estimation of the Number of Components in Factor and Principal Components,” Technometrics, 20, 397–405.
- Zhang, P. (1993), “Model Selection via Multifold Crossvalidation,” The Annals of Statistics, 21, 299–313.
- Zou, H., and Hastie, T. (2005), “Regularization and Variable Selection via The Elastic-Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320.
- Zou, H., Hastie, T., and Tibshirani, R. (2006), “Sparse Principal Component Analysis,” Journal of Computational and Graphical Statistics, 15, 265–286.