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Linear Discriminant Analysis (LDA) is a feature extraction technique aimed at maximizing between-class separability while minimizing within-class variability. It relies on calculating the between-class and within-class scatter matrices to determine the optimal projection matrix. Unlike PCA, which focuses on data representation, LDA emphasizes directions that enhance discrimination between distinct groups.

Author

muntaseer.jaheen.2003

Language

EN