What is Isomap?

How to learn from and reduce complex and high-dimensional shapes.

Introduction

We cannot visualize high-dimensional data above 3 dimensions. So what do we do when we are faced with this situation that is commonplace in nearly every Data Science application? Dimension reduction techniques like PCA often fail because there is a simple assumption to these methods: the data can be linearly reduced. However, for most types of high dimensional data, there is likely a non-linear relationship and therefore we need to maintain this…