Also 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
via PubMed
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.
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via Wikidata sitelinks · CC0
Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).