A Framework of Dimensionality Reduction Utilizing PCA for Neural Network Prediction

  • G. Ravi KumarEmail author
  • K. Nagamani
  • G. Anjan Babu
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)


This paper proposes the utilization of Principal Component Analysis (PCA) to decrease high-dimensional information and to enhance the prescient execution of the Neural Network machine learning model. Tests are done on a high-dimensional dataset, in which the errand is to recognize an objective. The investigations demonstrate that the utilization of this PCA strategy can enhance the execution of machine learning in the arrangement of high-dimensional information. It is broadly utilized in a large portion of the example acknowledgment applications like face acknowledgment, picture pressure, and for discovering designs in high-dimensional information. The work in this paper includes highlight decreased utilization of PCA pursued by the order which is finished utilizing Neural Network calculation. The exact outcomes exhibit Neural Network order with PCA is a productive characterization for extensive datasets.


  1. 1.
    X. Lei, A novel feature extraction method assembled with PCA and ICA for network intrusion detection. Comput. Sci. Technol. Appl. IFCSTA 3, 31–34 (2003)Google Scholar
  2. 2.
    Li Kan, Lui Yushu, Agent based data mining framework for the high dimensional environment. J. Beijing Inst. Technol. 14, 113–116 (2004)Google Scholar
  3. 3.
    A.I. Markos, M.G. Vozalis, K.G. Margaritas, An optimal scaling approach to collaborative filtering using categorical principal component analysis and neighborhood formation. Artif. Intell. Appl. Innov. (AIAI 2010), 339, 22–29, SpringerGoogle Scholar
  4. 4.
    L. Marquez, T. Hill, M. Connor, W. Remus, Neural networks models for forecast a review. In: IEEE Proceedings of the 25th Hawaii International Conference on System Sciences, Vol. 4, pp. 494–498 (1992)Google Scholar
  5. 5.
    N.M. Nawi, R.S. Ransing, An improved learning algorithm based on the Broyden fletcher Goldfarb Shanno (BFGS) method for back propagation neural networks. In: Sixth International Conference on Intelligent Systems Design and Applications, Vol. 1, pp. 152–157, October 2006Google Scholar
  6. 6.
    Michael, N., Artificial Intelligence—A Guide to Intelligent Systems. 2nd Edn, Addison Wesley (2005)Google Scholar
  7. 7.
    UCI Machine Learning Repository.
  8. 8.
    Y. Pang, Y. Yuan, X. Li, Effective feature extraction in high dimensional space. IEEE Trans. Syst. (2008)Google Scholar
  9. 9.
    J. Yan, B. Zhang, N. Liu, S. Yan, Z. Chen, Effective and efficient dimensionality reduction for large scale and streaming data processing. IEEE Trans. Knowl. Data Eng. 18(3), 320–333 (2006)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Rayalaseema UniversityKurnoolIndia
  2. 2.Sri Venkateshwara UniversityTirupatiIndia

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