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LDA Tutorial: Dimensionality Reduction by Aiz Dan is a document available to read on EtoBox.

What is LDA Tutorial: Dimensionality Reduction about?

LDA aims to perform dimensionality reduction while preserving class discriminatory information as much as possible. For two classes, LDA finds the linear projection that maximizes separation between the projected class means, while minimizing variance within each class. This is achieved by maximizing the Fisher criterion, which is the ratio of between-class scatter to within-class scatter. The within-class and between-class scatter can be expressed using the within-class and between-class scatter matrices.

Author

Aiz Dan

Language

EN