Diffusion Map for Manifold Learning, Theory and Implementation - KDnuggets

'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. Manifold learning is an approach to non-linear dimensionality reduction. The basis for algorithms in manifold learning is that the dimensionality of many data sets is only artificially high 1.