Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Principal Component Analysis

  • Paolo GiordaniEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_154-1




Principal Component Analysis


Singular Value Decomposition


Nowadays, almost everything can be monitored and measured implying that huge amounts of data are usually available. A big problem to be solved concerns how to transform such data into information. In other words, it is crucial to extract relevant information hidden in data sets. This is in general the main goal of statistical methods such as Principal Component Analysis (PCA). PCA is a well-known tool often used for the analysis of a numerical data set concerning a number of objects with respect to several variables (features). Its aim is to synthesize the data set in terms of the so-called components, i.e., unobserved variables expressed as linear combinations of the observed ones. These components are orthogonal and are found in such a way that they optimize a certain algebraic criterion (that we will...


Principal Component Analysis Singular Value Decomposition Component Score Spectral Decomposition Component Loading 
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Copyright information

© Springer Science+Business Media LLC 2016

Authors and Affiliations

  1. 1.Department of Statistical SciencesSapienza University of RomeRomeItaly

Section editors and affiliations

  • Suheil Khoury
    • 1
  1. 1.American University of SharjahSharjahUnited Arab Emirates