How does PCA relate to matrix factorization techniques?

PCA is a form of low-rank matrix factorization using orthogonal constraints.

PCA decomposes data matrix into orthogonal components that approximate original data with lower rank. This is closely related to matrix factorization methods used in recommendation systems, but PCA enforces orthogonality and variance maximization constraints.