DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Understand

Generating interpretable visualizations from complex data is a common problem in many applications.

• Two key ingredients for tackling this issue are clustering and representation learning.
• However, current methods do not yet successfully combine the strengths of these two approaches.
• Existing representation learning models which rely on latent topological structure such as self-organising maps, exhibit markedly lower clustering performance compared to recent deep clustering methods.