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:

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.