方法对比

	线性判别分析 (LDA)	二次判别分析 (QDA)
领域	机器学习	机器学习
方法族	Latent structure	Latent structure
起源年份	1936	1939
提出者	Fisher, R. A.	Classical Gaussian discriminant analysis (Fisher / Welch lineage)
类型	Supervised dimensionality reduction and linear classifier	Generative Gaussian classifier
开创性文献	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
别名	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	QDA, quadratic classifier, kuadratik diskriminant analizi
相关	4	2
摘要	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.