Linear Discriminant Analysis (LDA) is frequently used for dimensionality reduction in classification tasks. Similar to Kohonen maps, LDA helps in classifying high-dimensional data by transforming it to make classes more distinct. Specifically, LDA projects data onto a lower-dimensional subspace to optimize class separation, with the subspace dimension never exceeding the number of classes.

Comparison with PCA highlights that while PCA maximizes variance in data axes, LDA focuses on axes that distinctively separate data classes. QDA, a generalization of LDA, does not assume homogeneous class covariances, leading to more parameters for estimation. Unlike LDA, ANOVA uses categorical independent variables and continuous dependent variables for linear combination expressions.

LDA involves diagonalizing the within-class scatter matrix to get eigenvalues ...
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