# Dissimilarity Mixture Autoencoder for Deep Clustering

arXiv: https://arxiv.org/abs/2006.08177 · HTML: https://mlsanity.com/paper/2006.08177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [Deep Bag-of-Sub-Emotions for Depression Detection in Social Media](https://mlsanity.com/paper/2103.01334/) (2021)

## Abstract (arXiv)

The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture. It internally represents a dissimilarity mixture model (DMM) that extends classical methods like K-Means, Gaussian mixture models, or Bregman clustering to any convex and differentiable dissimilarity function through the reinterpretation of probabilities as neural network representations. DMAE can be integrated with deep learning architectures into end-to-end models, allowing the simultaneous estimation of the clustering and neural network's parameters. Experimental evaluation was performed on image and text clustering benchmark datasets showing that DMAE is competitive in terms of unsupervised classification accuracy and normalized mutual information. The source code with the implementation of DMAE is publicly available at: https://github.com/juselara1/dmae