title: Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps
abstract: 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.
authors: Laura Manduchi Matthias Hüser Gunnar Rätsch Vincent Fortuin
hIndex: Gunnar Rätsch 66