Linear Discriminant Analysis (LDA) is similar to PCA but tries to take class information into account to achieve a dimensionality reduction while keeping the class separation high. The result may be used in a subsequent classification. The method tries to maximize the ratio of between-class and within-class scatter in order to achieve a projection where data points of a class are close to each other and far from data points of other classes. More information can be found on Wikipedia.
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