Research output: Chapter in Book/Report/Conference proceedingForeword/postscript

5 Citations (Scopus)


Principal component analysis (PCA) is one of the widely used matrix factorization techniques for dimensionality reduction and revealing hidden factors that underlie sets of random variables, signals, or measurements. PCA is essentially a method for extracting individual signals from mixtures of signals. Its power resides in the physical assumptions that the different physical processes generate unrelated signals. The main aim of PCA is to reduce the dimensionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components (PCs), which are uncorrelated, and are ordered so that the first few retain most of the variation present in all of the original variables.

PCA research can be motivated by the open problems and continuing research on these problems, and hence a need to edit this book to report latest results on the topic...
Original languageEnglish
Title of host publicationAdvances in Principal Component Analysis
Subtitle of host publicationResearch and Development
EditorsGanesh R. Naik
PublisherSpringer Singapore
Number of pages2
ISBN (Electronic)9789811067044
ISBN (Print)9789811067037
Publication statusPublished - 2018
Externally publishedYes


  • Principal Component Analysis (PCA)
  • Source separation
  • Source identification
  • Dimensionality reduction
  • Nonlinear PCA
  • Kernel PCA
  • Sparse PCA
  • Time-frequency signal
  • Pattern recognition


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