principal component analysis
Sign in to saveAlso known as PCA, principal components analysis
conversion of a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components
Research
88,430 papers- What is principal component analysis?Nature biotechnology · 2008
- Principal Component Analysis (PCA).La Tunisie medicale · 2021
- Principal Component Analysis in Dental Research.The International journal of oral & maxillofacial implants · 2025
- Principal Component Analysis on Recurrent Venous Thromboembolism.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis · 2019
- Principal component analysis.Transfusion · 2018
via PubMed
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Article
PCA of a multivariate Gaussian distribution centered at (1, 3) with a standard deviation of 3 in roughly the (0.866, 0.5) direction and of 1 in the orthogonal direction. The vectors shown are the eigenvectors of the covariance matrix scaled by the square root of the corresponding eigenvalue, and shifted so their tails are at the mean.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.
Connections
eigenvectors and eigenvalues
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correlation
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median
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resampling
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artificial neural network
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variance
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phase change
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probability distribution
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linear map
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statistical population
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chi-square test
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standard score
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multivariate normal distribution
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Pearson product-moment correlation coefficient
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contingency table
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power of a test
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online machine learning
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Interactive Data Language
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eigendecomposition of a matrix
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trend estimation
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