Machine Learning with Spark - Second Edition

Relationship with matrix factorization

PCA and SVD are both matrix factorization techniques, in the sense that they decompose a data matrix into subcomponent matrices, each of which has a lower dimension (or rank) than the original matrix. Many other dimensionality reduction techniques are based on matrix factorization.

You might remember another example of matrix factorization, that is, collaborative filtering, which we have already seen in Chapter 6, Building a Classification Model with Spark. Matrix factorization approaches to collaborative filtering work by factorizing the ratings matrix into two components: the user factor matrix and the item factor matrix. Each of these has a lower dimension than the original data, so these methods ...