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Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Generating visualizations and interpretations from high-dimensional data is a common problem in many fields. Two key approaches for tackling this problem are clustering and representation learning. There are very performant deep clustering models on the one hand and interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps, on the other hand. However, current methods do not yet successfully combine these two approaches. We present a new deep architecture for probabilistic clustering, VarPSOM, and its extension to time series data, VarTPSOM, composed of VarPSOM modules connected by LSTM cells. 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 inducing an interpretable representation. Moreover, on the medical time series, VarTPSOM successfully predicts future trajectories in the original data space.

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