Step-by-Step Signal Processing with Machine Learning: Manifold Learning

In my first article on signal processing using machine learning, I introduced Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for dimensionality reduction. We were able to see how these methods can be used to reduce the number of features in our data. However, they are linear methods: they do not always perform well when there are nonlinear relationships within our data. This is where manifold learning comes in. A manifold is any space that is locally Euclidean.