Deep Embedded SOM: Joint Representation Learning and Self-Organization

In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map. Our model is composed of an autoencoder and a custom SOM layer that are optimized in a joint training procedure, motivated by the idea that the SOM prior could help learning SOM-friendly representations. We evaluate SOM-based models in terms of clustering quality and unsupervised clustering accuracy, and study the benefits of joint training.

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