Download PDFOpen PDF in browser

Principal Component Analysis

EasyChair Preprint no. 4384

5 pagesDate: October 13, 2020


Principal Component analysis or aka PCA is one of the most important dimensionality reduction technique out there. This paper is devoted towards why we need PCA, what are the steps to be taken and what are the benefits of using Principal component analysis. While in Data Exploratory Analysis we need to reduce the dimension in such a way that the maximum of what we need is to be captured.

Keyphrases: Data Exploratory Analysis, dimension reduction, Principal Component Analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Arun Kumar and Pankaj Kumar Saini},
  title = {Principal Component Analysis},
  howpublished = {EasyChair Preprint no. 4384},

  year = {EasyChair, 2020}}
Download PDFOpen PDF in browser