UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Dimension reduction seeks to produce a low dimensional representation of high dimensional data that preserves relevant structure (relevance often being application dependent). Dimension reduction is an important problem in data science for both visualization, and as a potential pre-processing step for machine learning. As a fundamental technique for both visualization and preprocessing, dimension reduction is being applied in a broadening range of fields and on ever increasing sizes of datasets. It is thus desirable to have an algorithm that is both scalable to massive data and able to cope with the diversity of data available. Dimension reduction algorithms tend to fall into two categories; those that seek to preserve the distance structure within the data or those that favor the preservation of local distances over global distance.