Fast ($\sim N$) Diffusion Map Algorithm

Abstract

In this work we explore parsimonious manifold learning techniques, specifically for Diffusion-maps. We demonstrate an algorithm and it's implementation with computational complexity (in both time and memory) of $\sim N$, with $N$ representing the number-of-samples. These techniques are essential for large-scale unsupervised learning tasks without any prior assumptions, due to sampling theorem limitations.