# Variable Clustering via Distributionally Robust Nodewise Regression

arXiv: https://arxiv.org/abs/2212.07944 · HTML: https://mlsanity.com/paper/2212.07944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Distributionally Robust Optimization: A Review](https://mlsanity.com/paper/1908.05659/) (2019)
- [Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit](https://mlsanity.com/paper/1507.01238/) (2015)
- [Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering](https://mlsanity.com/paper/1605.02633/) (2016)
- [Practical sketching algorithms for low-rank matrix approximation](https://mlsanity.com/paper/1609.00048/) (2016)
- [Robust subspace clustering](https://mlsanity.com/paper/1301.2603/) (2013)
- [Noisy Sparse Subspace Clustering](https://mlsanity.com/paper/1309.1233/) (2013)
- [Parsimonious modeling with Information Filtering Networks](https://mlsanity.com/paper/1602.07349/) (2016)
- [The multiplex dependency structure of financial markets](https://mlsanity.com/paper/1606.04872/) (2016)
- [Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods](https://mlsanity.com/paper/1406.0496/) (2014)

## Abstract (arXiv)

We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To solve the latter problem, we derive a convex relaxation, provide a data-driven approach for selecting the size of the robust region, and develop an ADMM algorithm for efficient implementation. We validate our method in extensive numerical studies and demonstrate its superior performance.