# Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning

arXiv: https://arxiv.org/abs/2110.04683 · HTML: https://mlsanity.com/paper/2110.04683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling](https://mlsanity.com/paper/1806.10175/) (2018)
- [K-Deep Simplex: Deep Manifold Learning via Local Dictionaries](https://mlsanity.com/paper/2012.02134/) (2020)
- [Deep Clustering Based on a Mixture of Autoencoders](https://mlsanity.com/paper/1812.06535/) (2018)
- [Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders](https://mlsanity.com/paper/1807.04734/) (2018)
- [On the convergence of group-sparse autoencoders](https://mlsanity.com/paper/2102.07003/) (2021)
- [Gaussian Process Convolutional Dictionary Learning](https://mlsanity.com/paper/2104.00530/) (2021)

## Built on by (most cited first, 1 of 1)

- [EGRC-Net: Embedding-induced Graph Refinement Clustering Network](https://mlsanity.com/paper/2211.10627/) (2022)

## Abstract (arXiv)

State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures. Many of these networks require a large number of parameters and suffer from a lack of interpretability, due to the black-box nature of the auto-encoders. We introduce Mixture Model Auto-Encoders (MixMate), a novel architecture that clusters data by performing inference on a generative model. Derived from the perspective of sparse dictionary learning and mixture models, MixMate comprises several auto-encoders, each tasked with reconstructing data in a distinct cluster, while enforcing sparsity in the latent space. Through experiments on various image datasets, we show that MixMate achieves competitive performance compared to state-of-the-art deep clustering algorithms, while using orders of magnitude fewer parameters.