Principal components analysis
Principal components analysis (PCA) is a mathematical process that takes a bunch of data points (like numbers from a test grade, or measurements from a science experiment) and finds patterns in it. It can help make data easier to understand, and can help you answer questions about what the data means. For example, if you had a bunch of test scores from a math class, PCA could help you figure out what kinds of questions everyone in the class did the best on, or what kinds of questions everyone struggled with the most.
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CUR matrix approximation,
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Directional component analysis,
Dynamic mode decomposition,
Eigenface,
Expectation–maximization algorithm,
Factor analysis of mixed data,
Factorial code,
Functional principal component analysis,
Geometric data analysis