References
[1]. Anowar, F., & Sadaoui, S. (2020, October).
Incremental neural-network learning for big fraud data. In
2020 IEEE International Conference on Systems, Man,
and Cybernetics (SMC) (pp. 3551-3557). IEEE. https://doi.org/10.1109/SMC42975.2020.9283136
[2]. Ayesha, S., Hanif, M. K., & Talib, R. (2020). Overview
and comparative study of dimensionality reduction
techniques for high dimensional data. Information
Fusion, 59, 44-58. https://doi.org/10.1016/j.inffus.2020.01.005
[3]. Brigadir, I., Greene, D., Cross, J. P., & Cunningham, P.
(2016, September). Dimensionality reduction and
visualisation tools for voting record. In 24th Irish
Conference on Artificial Intelligence and Cognitive
Science (AICS'16), University College Dublin, Ireland, 20-21 September 2016. CEUR Workshop Proceedings.
[4]. Chormunge, S., & Jena, S. (2018). Correlation based
feature selection with clustering for high dimensional
data. Journal of Electrical Systems and Information
Technology, 5(3), 542-549. https://doi.org/10.1016/j.jesit.2017.06.004
[5]. Cunningham, J. P., & Ghahramani, Z. (2015). Linear
dimensionality reduction: Sur vey, insights, and
generalizations. The Journal of Machine Learning
Research, 16(1), 2859-2900.
[6]. Huang, X., Wu, L., & Ye, Y. (2019). A review on
dimensionality reduction techniques. International
Journal of Pattern Recognition and Artificial Intelligence,
33(10), 1950017. https://doi.org/10.1142/S0218001419500174
[7]. Kumar, G. R., & Nagamani, K. (2018). Banknote
authentication system utilizing deep neural network with
PCA and LDA machine learning techniques. International
Journal of Recent Scientific Research, 9(12), 30036-30038.
[8]. Meng, C., Zeleznik, O. A., Thallinger, G. G., Kuster, B.,
Gholami, A. M., & Culhane, A. C. (2016). Dimension
reduction techniques for the integrative analysis of multiomics
data. Briefings in Bioinformatics, 17(4), 628-641.
https://doi.org/10.1093/bib/bbv108
[9]. Salo, F., Nassif, A. B., & Essex, A. (2019). Dimensionality
reduction with IG-PCA and ensemble classifier for network
intrusion detection. Computer Networks, 148, 164-175. https://doi.org/10.1016/j.comnet.2018.11.010
[10]. Shah, F. P., & Patel, V. (2016, March). A review on
feature selection and feature extraction for text
classification. In 2016 International Conference on
Wireless Communications, Signal Processing and
Networking (WiSPNET) (pp. 2264-2268). IEEE. https://doi.org/10.1109/WiSPNET.2016.7566545
[11]. Sonawale, S. A., & Ade, R. (2015). Dimensionality
reduction: an effective technique for feature selection.
International Journal of Computer Applications, 117(3),
18–23.
[12]. Sorzano, C. O. S., Vargas, J., & Montano, A. P. (2014).
A survey of dimensionality reduction techniques. arXiv
preprint arXiv:1403.2877, 1-35. https://doi.org/10.48550/arXiv.1403.2877
[13]. Tu, H. T., Phan, T. T., & Nguyen, K. P. (2017, July). An
adaptive latent semantic analysis for text mining. In 2017
International Conference on System Science and
Engineering (ICSSE) (pp. 588-593). IEEE. https://doi.org/10.1109/ICSSE.2017.8030943
[14]. Zheng, A., & Casari, A. (2018). Feature Engineering
for Machine Learning: Principles and Techniques for Data
Scientists. "O'Reilly Media, Inc.".
