Linear Discriminant Analysis

Linear Discriminant Analysis (LDA) is a statistical method used to find a linear combination of features that characterizes or separates two or more classes of objects or events. The goal of LDA is to project the original high-dimensional data onto a lower-dimensional space while preserving the class-discriminatory information. This is achieved by maximizing the ratio of the between-class variance to the within-class variance. LDA is commonly used in pattern recognition, machine learning, and computer vision applications to reduce the dimensionality of the data and improve classification accuracy. It is also used in feature extraction, where the reduced set of features can be used to train other classifiers or models.
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