DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps.

Generating visualizations and interpretations from high-dimensional data is a common problem in many applications. Two key approaches for tackling this problem are clustering and representation learning. On the one hand, there are very performant deep clustering models, such as DEC and IDEC. On the other hand, there are interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps. However, current methods do not yet successfully combine these two approaches. We present 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. We show that they 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. Moreover, on medical time series, T-DPSOM successfully predicts future trajectories in the original data space.