# Clustering based on Mixtures of Sparse Gaussian Processes

arXiv: https://arxiv.org/abs/2303.13665 · HTML: https://mlsanity.com/paper/2303.13665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Deep Convolutional Networks as shallow Gaussian Processes](https://mlsanity.com/paper/1808.05587/) (2018)
- [Deep Subspace Clustering Networks](https://mlsanity.com/paper/1709.02508/) (2017)
- [CNN-Based Joint Clustering and Representation Learning with Feature Drift Compensation for Large-Scale Image Data](https://mlsanity.com/paper/1705.07091/) (2017)
- [Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding](https://mlsanity.com/paper/1905.11741/) (2019)
- [Deep Clustering Based on a Mixture of Autoencoders](https://mlsanity.com/paper/1812.06535/) (2018)

## Abstract (arXiv)

Creating low dimensional representations of a high dimensional data set is an important component in many machine learning applications. How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction. When a probabilistic model is desired, one possible solution is to use the mixture models in which both cluster indicator and low dimensional space are learned. Our algorithm is based on a mixture of sparse Gaussian processes, which is called Sparse Gaussian Process Mixture Clustering (SGP-MIC). The main advantages to our approach over existing methods are that the probabilistic nature of this model provides more advantages over existing deterministic methods, it is straightforward to construct non-linear generalizations of the model, and applying a sparse model and an efficient variational EM approximation help to speed up the algorithm.