Salīdzināt metodes

	Lineārā diskriminanta analīze (LDA)	Kvadrātiskā diskriminantanalīze (QDA)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Latent structure	Latent structure
Izcelsmes gads	1936	1939
Autors	Fisher, R. A.	Classical Gaussian discriminant analysis (Fisher / Welch lineage)
Tips	Supervised dimensionality reduction and linear classifier	Generative Gaussian classifier
Pirmavots	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0
Citi nosaukumi	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	QDA, quadratic classifier, kuadratik diskriminant analizi
Saistītās	4	2
Kopsavilkums	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.	Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.