# Edgewise outliers of network indexed signals

arXiv: https://arxiv.org/abs/2307.11239 · HTML: https://mlsanity.com/paper/2307.11239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [How to learn a graph from smooth signals](https://mlsanity.com/paper/1601.02513/) (2016)
- [A Time-Vertex Signal Processing Framework](https://mlsanity.com/paper/1705.02307/) (2017)
- [Minimum Covariance Determinant and Extensions](https://mlsanity.com/paper/1709.07045/) (2017)
- [Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries](https://mlsanity.com/paper/2007.00041/) (2020)
- [Graph Signal Processing -- Part I: Graphs, Graph Spectra, and Spectral Clustering](https://mlsanity.com/paper/1907.03467/) (2019)
- [Fast robust correlation for high-dimensional data](https://mlsanity.com/paper/1712.05151/) (2017)
- [The Minimum Regularized Covariance Determinant estimator](https://mlsanity.com/paper/1701.07086/) (2017)
- [Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications](https://mlsanity.com/paper/2001.00426/) (2020)
- [Outlier detection in non-elliptical data by kernel MRCD](https://mlsanity.com/paper/2008.02046/) (2020)
- [Real-time outlier detection for large datasets by RT-DetMCD](https://mlsanity.com/paper/1910.05615/) (2019)
- [Spatial Sign Correlation](https://mlsanity.com/paper/1403.7635/) (2014)

## Abstract (arXiv)

We consider models for network indexed multivariate data involving a dependence between variables as well as across graph nodes. In the framework of these models, we focus on outliers detection and introduce the concept of edgewise outliers. For this purpose, we first derive the distribution of some sums of squares, in particular squared Mahalanobis distances that can be used to fix detection rules and thresholds for outlier detection. We then propose a robust version of the deterministic MCD algorithm that we call edgewise MCD. An application on simulated data shows the interest of taking the dependence structure into account. We also illustrate the utility of the proposed method with a real data set.