Fisher线性鉴别分析方法(FLDA)

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Fisher线性鉴别分析方法(FLDA)

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Fisher's linear discriminant analysis (FLDA) is a statistical technique used to find a linear combination of features that characterizes or separates two or more classes of objects or events. It is a supervised learning technique that is commonly used in pattern recognition and machine learning applications.

It is based on the idea of maximizing the ratio of between-class scatter to within-class scatter, which means that it tries to find a projection of the data that maximizes the distance between the means of different classes while minimizing the variance within each class.

FLDA has been successfully applied in various fields, such as face recognition, image classification, and bioinformatics. It is a powerful method for reducing the dimensionality of data and extracting useful features that can be used for classification and other tasks.