# Scalable and Incremental Learning of Gaussian Mixture Models

arXiv: https://arxiv.org/abs/1701.03940 · HTML: https://mlsanity.com/paper/1701.03940/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2017.

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

- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [Forensic Data Analytics for Anomaly Detection in Evolving Networks](https://mlsanity.com/paper/2308.09171/) (2023)

## Abstract (arXiv)

This work presents a fast and scalable algorithm for incremental learning of Gaussian mixture models. By performing rank-one updates on its precision matrices and determinants, its asymptotic time complexity is of BigONKD2 for N data points, K Gaussian components and D dimensions. The resulting algorithm can be applied to high dimensional tasks, and this is confirmed by applying it to the classification datasets MNIST and CIFAR-10. Additionally, in order to show the algorithm's applicability to function approximation and control tasks, it is applied to three reinforcement learning tasks and its data-efficiency is evaluated.