# Deep Clustering Based on a Mixture of Autoencoders

arXiv: https://arxiv.org/abs/1812.06535 · HTML: https://mlsanity.com/paper/1812.06535/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering](https://mlsanity.com/paper/2105.01899/) (2021)
- [You Never Cluster Alone](https://mlsanity.com/paper/2106.01908/) (2021)
- [End-to-end Differentiable Clustering with Associative Memories](https://mlsanity.com/paper/2306.03209/) (2023)
- [Deep Clustering with Associative Memories](https://mlsanity.com/paper/2601.00963/) (2026)
- [Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning](https://mlsanity.com/paper/2110.04683/) (2021)
- [Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm](https://mlsanity.com/paper/2209.04213/) (2022)
- [Clustering based on Mixtures of Sparse Gaussian Processes](https://mlsanity.com/paper/2303.13665/) (2023)

## Abstract (arXiv)

In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC) algorithm based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. The clustering algorithm jointly learns the nonlinear data representation and the set of autoencoders. The optimal clustering is found by minimizing the reconstruction loss of the mixture of autoencoder network. Unlike other deep clustering algorithms, no regularization term is needed to avoid data collapsing to a single point. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.