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PCA and LDA for Dimensionality Reduction by Kunapuli Poojitha is a document available to read on EtoBox.

What is PCA and LDA for Dimensionality Reduction about?

PCA (Principal Component Analysis) reduces dataset dimensionality by finding orthogonal dimensions ranked by variance. LDA (Linear Discriminant Analysis) is another feature reduction technique commonly used in machine learning and is similar to PCA. Both methods aim to simplify data while preserving essential information.

Author

Kunapuli Poojitha

Language

EN