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Comparative Analysis of Dimensionality Reduction Techniques for Machine Learning

Authors(4) :-Santhosh Voruganti, Karnati Ramyakrishna, Srilok Bodla, E. Umakanth

Dimensionality reduction as a pre-processing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. Aim of this paper is to reduce the dimensionality of the dataset without the loss of any information from the datasets.we have implemented three dimensionality reduction algorithms.so this three algorithms are performed on two datasets, Iris and Wines datasets and the results are analyzed.
Santhosh Voruganti, Karnati Ramyakrishna, Srilok Bodla, E. Umakanth
  1. Tom M. Mitchell, "Machine Learning ",McGraw Hill, 1997
  2. Stephen Marsland, "Machine Learning - An Algorithmic Perspective ", CRC Press, 2009. Margaret H Dunham, "Data Mining", Pearson Edition, 2003.
  3. GalitShmueli, Nitin R Patel, Peter C Bruce, "Data Mining, 2007 for Business Intelligence", Wiley India Edition.
  4. Rajjall Shinghal, "Pattern Recognition ",Oxford University Press, 2006.
  5. Ashish Kumar and Avinash Paul the authors of the book Mastering Text Mining with R,
  6. Nonlinear Dimensionality ReductionAuthors: Lee, John A., Verleysen, MichelMathematical Methodologies in Pattern Recognition and MachiLearningEditors: Latorre Carmona, Pedro, S-nchez, J. Salvador, Fred - 502 854.
  7. Understanding Machine Learning: From Theory to Algorithms Textbook by Shai Ben-David and ShaiShalev-Shwartz.
  8. Foundations of Machine Learning Textbook by Afshin Rostamizadeh, Ameet Talwalkar, and Mehryar Mohri.K-glDonald, C. WunschAndrei, Y. ZinovyevChristopher.
  9. A survey of dimension reduction techniques Imola K. FodorCenter for Applied ScientficComputing, Lawrence Livermore National Laboratory P.O. Box 808, L-560, Livermore, CA 94551.
  10. IEEEI.T Jolliffe, Principal Component Analysis, Springer,second edition.
  11. Chao Shi and Chen Lihui, 2005. Feature dimension reduction for microarray data analysis using locally linear embedding, 3rdAsia Pacific Bioinformatics Conference, pp. 211-217.
  12. Dimensionality Reduction for Data Mining-Techniques, Applications and Trends- Jieping Ye, HuanLiuArizona State University.
  13. Principal Component Analysis With Complex Kernels Athanasios Papaioannou, Student Member, IEEE, Stefanos Zafeiriou, Member, IEEE
  14. A review of feature selection methods with Applications A. Jovic*, K. Brkic* and N. Bogunovic*
  15. Nonlinear Multimode Industrial Process Fault Detection Using Modified Kernel Principal Component Analysis XIAOGANG DENG , NA ZHONG, AND LEI WANG College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
Publication Details
  Published in : Volume 4 | Issue 8 | May-June 2018
  Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 364-369
Manuscript Number : IJSRST184871
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Santhosh Voruganti, Karnati Ramyakrishna, Srilok Bodla, E. Umakanth, "Comparative Analysis of Dimensionality Reduction Techniques for Machine Learning", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.364-369, May-June-2018
URL : http://ijsrst.com/IJSRST184871