方法对比

	线性判别分析 (LDA	K-Nearest Neighbors
领域	统计学	机器学习
方法族	Hypothesis test	Machine learning
起源年份	1936	1967
提出者	Ronald A. Fisher	Cover, T.M. & Hart, P.E.
类型	Parametric linear classifier / dimensionality reduction	Instance-based (non-parametric) learning
开创性文献	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
别名	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning
相关	7	5
摘要	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.	K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.