Dissimilarity Mixture Autoencoder for Deep Clustering

Tensorflow implementation of the Dissimilarity Mixture Autoencoder:

• Juan S. Lara and Fabio A. González. "Dissimilarity Mixture Autoencoder for Deep Clustering" arXiv preprint arXiv:2006.08177 (2020).

Abstract

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 Bregman clustering to any convex and differentiable dissimilarity function through the reinterpretation of probabilistic notions as neural network components. Likewise, it leverages from unsupervised representation learning, allowing a simultaneous learning of the clusters 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.

Usage and Documentation

You can check the official dmae documentation.

Gallery and Examples

• Deep architecture:

  

• Clustering examples:

• Probabilistic interpretations:

These examples and the paper replication experiments can be found in the examples folder.

Installation

You can install dmae from PyPi using pip, building from source or pulling a preconfigured docker image.

PyPi

To install dmae using pip you can run the following command:

pip install dmae

(optional) If you have an environment with the nvidia drivers and CUDA, you can instead run:

pip install dmae-gpu

Source

You can clone this repository:

git clone https://github.com/juselara1/dmae.git

Install the requirements:

pip install -r requirements.txt

(optional) If you have an environment with the nvidia drivers and CUDA, you can instead run:

pip install -r requiremets-gpu.txt

Finally, you can install dmae via setuptools:

pip install --no-deps .

Docker

You can pull a preconfigured docker image with dmae from DockerHub:

docker pull juselara/dmae:latest

(optional) If you have an environment with the nvidia drivers installed, you can instead run:

docker pull juselara/dmae:latest-gpu

Citation

@misc{lara2020dissimilarity,
      title={Dissimilarity Mixture Autoencoder for Deep Clustering}, 
      author={Juan S. Lara and Fabio A. González},
      year={2020},
      eprint={2006.08177},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}