LDA

2000년 도입 · 논문 459편에서 사용

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification. Extracted from Wikipedia Source: Paper: Linear Discriminant Analysis: A Detailed Tutorial Public version: Linear Discriminant Analysis: A Detailed Tutorial