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Review on Dimensionality Reduction Techniques

  • Dhruv ChauhanEmail author
  • Rejo Mathews
Conference paper
  • 37 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

With the increasing use of Machine day by day, data analysts’ job has increased drastically. With the data gathered from millions of machines and sensors, modern day datasets becomes wealthier in information. This makes the data to be high dimensional and it is quite common to see datasets with hundreds of features. One of the biggest problems that data analysts face is dealt with high dimensional data. Without a major loss of information, data can be effectively reduced to a much smaller number of variables. This method of reducing variables is known as Dimensionality Reduction. The objective of this paper is to review methods used for reducing Dimensionality.

Keywords

Data Mining Dimensionality reduction Machine learning Principle component analysis 

References

  1. 1.
    Maaten, L.V.D., Postma, E., Herik, J.V.D.: Dimensionality reduction: a comparative review. Tilburg centre for Creative Computing, 26 October 2009Google Scholar
  2. 2.
    Saini, O., Sharma, S.: A review on dimension reduction techniques in data mining. Comput. Eng. Intell. Syst. 9(1), 7–14 (2018)Google Scholar
  3. 3.
    Singh, A.G., Asir, D., Leavline, E.J., Appavu, B.S.: An empirical study on dimensionality reduction and improvement of classification accuracy using feature subset selection and ranking. J. Theoret. Appl. Inf. Technol. (2012)Google Scholar
  4. 4.
    Shi, C., Chen, L.: Feature dimension reduction for microarray data analysis using locally linear embedding. In: International Symposium on Bioinformatics Research and Applications APBC, pp. 211–217 (2005)Google Scholar
  5. 5.
    Venkat, N.: The curse of dimensionality: inside out (2018)Google Scholar
  6. 6.
    Cunningham, J.P., Ghahramani, Z.: Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16, 2859–2900 (2015)Google Scholar
  7. 7.
    Ebied, H.: Feature extraction using PCA and kernel-PCA for face recognition. In: International Conference on Informatics and Systems (2012)Google Scholar
  8. 8.
    Sorzano, C.O.S., Vargas, J., Motano, A.P.: A survey of dimensionality reduction techniques. ArXiv (2014)Google Scholar
  9. 9.
    Konsorum, A., Jackel, N., Vidal, E., Laubenbacher, R.: Comparative analysis of Linear and Non Linear Dimension Reduction Techniques on Mass. Cold Spring Harbor Laboratory (2018)Google Scholar
  10. 10.
    Krivov, E., Belyeav, M.: Dimensionality reduction with isomap algorithm for EEG covariance matrices. In: International Winter Conference on Brain Computer Interface (2016)Google Scholar
  11. 11.
    Griparis, A., Faur, D., Datchu, M.: Feature space dimensionality reduction for the optimization of visualization methods. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2015)Google Scholar
  12. 12.
    Thomas, D., Oke, O., Smartt, C.: Statistical analysis in EMC using dimension reduction methods. In: 2014 IEEE International Symposium on Electromagnetic Compatibility (EMC) (2014)Google Scholar
  13. 13.
    Sancheti, P., Shedge, R., Pulgam, N.: Word-IPCA: an ımprovement in dimension reduction techniques. In: 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT) (2018)Google Scholar
  14. 14.
    Jatram, A., Biswas, B.: Dimension reduction using spectral methods in FANNY for fuzzy clustering of graphs. In: 2015 Eighth International Conference on Contemporary Computing (IC3) (2015)Google Scholar
  15. 15.
    Pei, Z.H., Shen, Q.: Local linear dimensionality reduction algorithm based on nonlinear manifolds decomposition. In: 2017 International Conference on Network and Information Systems for Computers (ICNISC) (2017)Google Scholar
  16. 16.
    Raj, J.S.: A comprehensive survey on the computational intelligence techniques and its applications. J. ISMAC 1(03), 147–159 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mukesh Patel School of Technology Management and EducationNMIMSMumbaiIndia

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