DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

This work presents a novel way to fit self-organizing maps with probabilistic cluster assignments, PSOM, a new deep architecture for Probabilistic clustering, DPSOM, and its extension to time series data, T-DPSOM, which achieve superior clustering performance compared to current deep clustering methods on static MNIST/Fashion-MNIST data as well as medical time series, while also inducing an interpretable representation.