Diffusion Map for manifold learning, Theory and Implementation

‘Curse of dimensionality’ is a well-known problem in Data Science, which often causes poor performance, inaccurate results, and, most importantly, a similarity measure break-down. The primary cause of this is because high dimensional datasets are typically sparse, and often a lower-dimensional structure or ‘Manifold’ would embed this data. So there is a non-linear relationship among the variables (or features or dimensions), which we need to learn to compute better similarity.

Read more at https://randomwalk.in/python/ml/2020/03/14/Diffusion-Map.html