Wiley StatsRef: Statistics Reference Online· 2014
Principal Component Analysis
- 1,257citations
- 2014year
Short summary
Principal Component Analysis (PCA) is a technique to reduce the dimensionality of large multivariate datasets by replacing original variables with fewer derived variables (principal components) that capture most of the original variability.
AI-generated from the title and abstract; the full text is not read.
Key points
- Reduces dimensionality of large multivariate datasets.
- Replaces 'p' original variables with 'q' principal components (q < p).
- Principal components are linear combinations of original variables.
- Aims to retain most of the original variability with fewer components.
- Requires choices on covariance/correlation, number of components, and normalization.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q , of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p . Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined.
The authors' abstract, as published at the source. Wiley StatsRef: Statistics Reference Online, 2014 · DOI ↗
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Field: Analytical Chemistry
Analytical ChemistryChemistry