eigen

Introduction In the first post in this series we discussed Expectation Maximization (EM) type algorithms. In the post prior to this one we discussed regularization and showed how it leads to a bias-variance trade off in OLS models.

Introduction Principal component analysis is one of the most commonly used techniques in statistical modeling and machine learning. In typical applications it serves as a (linear) dimensionality reduction, allowing one to project high dimensional data onto a lower dimensional subspace.