A Self-Organizing UMAP for Clustering

We introduce SOUMAP (Self-Organizing Uniform Manifold Approximation and Projection) as an alternative to regular Self-Organizing Map (SOM) learning intended to improve clustering of the resulting output space. Improvement is achieved by abandoning the SOM’s rigid lattice structure in favor of a more expressive output topology afforded by Uniform Manifold Approximation and Projection (UMAP), which is incrementally learned in conjunction with SOUMAP prototypes. As in regular SOM learning, the Hebbian connection formed between input and output spaces results in a topologically trustworthy low-dimensional embedding amenable to clustering. Through controlled experiments we show that SOUMAP’s more expressive “lattice” improves the quality of clusterings obtained from it.