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PCA and LDA 2 (i2tutorials)

What are the differences between PCA and LDA?

Both LDA and PCA are linear transformation techniques: LDA is a supervised whereas PCA is unsupervised and ignores class labels.

We can picture PCA as a technique that finds the directions of maximal variance:

PCA and LDA 1 (i2tutorials)

In contrast to PCA, LDA attempts to find a feature subspace that maximizes class separability. LDA makes assumptions about normally distributed classes and equal class covariances.

PCA and LDA 2 (i2tutorials)

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