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Encyclopedia of Social Network Analysis and Mining pp 1–14Cite as

Principal Component Analysis

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Synonyms

Data reduction method; PCA; Principal component method; Singular value decomposition

Glossary

PCA:

Principal Component Analysis

SVD:

Singular Value Decomposition

Definition

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...

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Author information

Authors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, P.le Aldo Moro, 5, Rome, Italy

    Paolo Giordani

Authors
  1. Paolo Giordani
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Corresponding author

Correspondence to Paolo Giordani .

Editor information

Editors and Affiliations

  1. Computer Science, University of Calgary, Calgary, Alberta, Canada

    Reda Alhajj

  2. University of Calgary Computer Science, Calgary, Canada

    Jon Rokne

Section Editor information

  1. American University of Sharjah, Sharjah, United Arab Emirates

    Suheil Khoury

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Giordani, P. (2016). Principal Component Analysis. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_154-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7163-9_154-1

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7163-9

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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