Review on Dimensionality Reduction Techniques

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


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.


Data Mining Dimensionality reduction Machine learning Principle component analysis 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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