Journal of Signal and Information Processing· 2013
An Overview of Principal Component Analysis
- 532citations
- 2013year
Short summary
Principal Component Analysis (PCA) is a statistical technique that uses orthogonal transformation to convert correlated variables into uncorrelated ones, reducing data dimensionality while preserving information.
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Key points
- PCA is a statistical technique for data dimensionality reduction.
- It uses orthogonal transformation to create uncorrelated variables from correlated ones.
- The method retains the majority of the original information in the reduced dimensions.
- Key underlying concepts include standard deviation, covariance, and eigenvectors.
AI-generated from the title and abstract; the full text is not read.
Abstract
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.
The authors' abstract, as published at the source. Journal of Signal and Information Processing, 2013 · DOI ↗
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Field: Analytical Chemistry
Analytical ChemistryChemistry