[
1. Breiman L. (1995). Better Subset Regression Using the Nonnegative Garrote. Technometrics, Vol. 37, No. 4, pp. 373-384.10.1080/00401706.1995.10484371
]Search in Google Scholar
[
2. Gajjar S., Kulahci M., Palazoglu A. (2017). Selection of non-zero loadings in sparse principal component analysis. Chemometrics and Intelligent Laboratory Systems, Vol. 162, pp. 160-171.10.1016/j.chemolab.2017.01.018
]Search in Google Scholar
[
3. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.10.1007/978-1-4614-7138-7
]Search in Google Scholar
[
4. Kaggle (2021a). Adult Income dataset. Available at https://www.kaggle.com/wenruliu/adult-income-dataset [01 July 2021].
]Search in Google Scholar
[
5. Kaggle (2021b). Monika dataset. Available at https://www.kaggle.com/ukveteran/who-monica-data [01 July 2021].
]Search in Google Scholar
[
6. Kim, S., Rattakorn, P. (2011). Unsupervised feature selection using weighted principal components. Expert Systems with Applications, Vol. 38, No. 5, pp. 5704-5710.10.1016/j.eswa.2010.10.063
]Search in Google Scholar
[
7. Maleki, N., Zeinali, Y., Niaki, S.T.A. (2020). A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection. Expert Systems with Applications, Vol. 164.10.1016/j.eswa.2020.113981
]Search in Google Scholar
[
8. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, New York.
]Search in Google Scholar
[
9. Pacheco, J., Casado, S., Porras, S. (2013). Exact methods for variable selection in principal component analysis: Guide functions and pre-selection. Computational Statistics & Data Analysis, Vol. 57, No. 1, pp. 95-111.10.1016/j.csda.2012.06.014
]Search in Google Scholar
[
10. Prieto-Moreno, A., Llanes-Santiago, O., García-Moreno, E. (2015). Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis. Journal of Process Control, Vol. 33, pp. 14-24.10.1016/j.jprocont.2015.06.003
]Search in Google Scholar
[
11. Rahoma, A., Imtiaz, S., Ahmed, S. (2021). Sparse principal component analysis using bootstrap method. Chemical Engineering Science, Vol. 246.10.1016/j.ces.2021.116890
]Search in Google Scholar
[
12. Salata, S., Grillenzoni, C. (2021). A spatial evaluation of multifunctional Ecosystem Service networks using Principal Component Analysis: A case of study in Turin, Italy. Ecological Indicators, Vol. 127, pp. 1-13.10.1016/j.ecolind.2021.107758
]Search in Google Scholar
[
13. Sharifzadeh, S., Ghodsi, A.,Clemmensen, L., Ersbll B. (2017). Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection. Engineering Applications of Artificial Intelligence, Vol. 65, pp. 168-177.10.1016/j.engappai.2017.07.004
]Search in Google Scholar
[
14. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society Series B (Statistical Methodology), Vol. 73, No. 3, pp. 267-268,10.1111/j.1467-9868.2011.00771.x
]Search in Google Scholar
[
15. Vincentarelbundock (2021). EPICA Dome C Ice Core 800KYr Temperature Estimates dataset. Available at https://vincentarelbundock.github.io/Rdatasets/datasets.html [01 July 2021].
]Search in Google Scholar
[
16. Vrigazova, B. (2021). Novel Approach to Choosing Principal Components Number in Logistic Regression. ENTRENOVA-ENTerprise REsearch InNOVAtion, Vol. 7, No. 1, pp. 1-12.10.54820/PUCR5250
]Search in Google Scholar
[
17. Vrigazova, B., Ivanov, I. (2020). Tenfold bootstrap procedure for support vector machines. Computer Science, Vol. 21, No. 2, pp. 241-257.10.7494/csci.2020.21.2.3634
]Search in Google Scholar
[
18. Zou, H. (2006). The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association, Vol. 101, No. 476, pp. 1418-1429.10.1198/016214506000000735
]Search in Google Scholar