# Challenges of cellwise outliers

arXiv: https://arxiv.org/abs/2302.02156 · HTML: https://mlsanity.com/paper/2302.02156/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2023.

## Built on by (most cited first, 5 of 5)

- [Casewise and Cellwise Robust Multilinear Principal Component Analysis](https://mlsanity.com/paper/2503.07327/) (2025)
- [Independent Component Analysis by Robust Distance Correlation](https://mlsanity.com/paper/2505.09425/) (2025)
- [On the distance between mean and geometric median in high dimensions](https://mlsanity.com/paper/2508.12926/) (2025)
- [Cellwise Outliers](https://mlsanity.com/paper/2604.14182/) (2026)
- [Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data](https://mlsanity.com/paper/2605.31345/) (2026)

## Abstract (arXiv)

It is well-known that real data often contain outliers. The term outlier typically refers to a case, that is, a row of the n × d data matrix. In recent times a different type has come into focus, the cellwise outliers. These are suspicious cells (entries) that can occur anywhere in the data matrix. Even a relatively small proportion of outlying cells can contaminate over half the rows, which is a problem for rowwise robust methods. In this article we discuss the challenges posed by cellwise outliers, and some methods developed so far to deal with them. We obtain new results on cellwise breakdown values for location, covariance and regression. We also propose a cellwise robust method for correspondence analysis, with real data illustrations. The paper concludes by formulating some points for debate.