# High-dimensional cluster analysis with the Masked EM Algorithm

arXiv: https://arxiv.org/abs/1309.2848 · HTML: https://mlsanity.com/paper/1309.2848/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2013.

## Built on by (most cited first, 3 of 3)

- [High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees](https://mlsanity.com/paper/2104.00245/) (2021)
- [A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances](https://mlsanity.com/paper/2104.04599/) (2021)
- [HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings](https://mlsanity.com/paper/2507.17224/) (2025)

## Abstract (arXiv)

Cluster analysis faces two problems in high dimensions: first, the `curse of dimensionality' that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. In many applications, only a small subset of features provide information about the cluster membership of any one data point, however this informative feature subset may not be the same for all data points. Here we introduce a `Masked EM' algorithm for fitting mixture of Gaussians models in such cases. We show that the algorithm performs close to optimally on simulated Gaussian data, and in an application of `spike sorting' of high channel-count neuronal recordings.