Principal Component Analysis

This chapter considers classical and robust principal component analysis (PCA). Principal component analysis is used to explain the dispersion structure with a few linear combinations of the original variables, called principal components. These linear combinations are uncorrelated if \(\varvec{S}\) or \(\varvec{R}\) is used as the dispersion matrix. The analysis is used for data reduction and interpretation.

• Publication year

  2017

• Venue

  Encyclopedia of Machine Learning and Data Mining

• Publication date

  2017-03-03

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  DOI 10.1007/978-1-4899-7687-1_665
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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