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Linear Discriminant Analysis (Classification)

Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.

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Linear Discriminant Analysis (LDA — Classification)

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Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. · DOI 10.1111/j.1469-1809.1936.tb02137.x

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See alsoFactor Analysismachine-suggested · Relational suggestion, not evidence.See alsoK-Nearest Neighborsmachine-suggested · Relational suggestion, not evidence.See alsoLogistic Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyMANOVAmachine-suggested · Relational suggestion, not evidence.See alsoNaive Bayesmachine-suggested · Relational suggestion, not evidence.See alsoPrincipal Component Analysismachine-suggested · Relational suggestion, not evidence.See alsoSupport Vector Machinemachine-suggested · Relational suggestion, not evidence.