# Robust convex biclustering with a tuning-free method

arXiv: https://arxiv.org/abs/2212.03122 · HTML: https://mlsanity.com/paper/2212.03122/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Adaptive Huber Regression](https://mlsanity.com/paper/1706.06991/) (2017)
- [User-Friendly Covariance Estimation for Heavy-Tailed Distributions](https://mlsanity.com/paper/1811.01520/) (2018)
- [Convex Biclustering](https://mlsanity.com/paper/1408.0856/) (2014)
- [Robust Hierarchical Clustering](https://mlsanity.com/paper/1401.0247/) (2014)
- [Resistant convex clustering: How does the fusion penalty enhance resistantance?](https://mlsanity.com/paper/1906.09581/) (2019)

## Built on by (most cited first, 1 of 1)

- [Biconvex Biclustering](https://mlsanity.com/paper/2604.03936/) (2026)

## Abstract (arXiv)

Biclustering is widely used in different kinds of fields including gene information analysis, text mining, and recommendation system by effectively discovering the local correlation between samples and features. However, many biclustering algorithms will collapse when facing heavy-tailed data. In this paper, we propose a robust version of convex biclustering algorithm with Huber loss. Yet, the newly introduced robustification parameter brings an extra burden to selecting the optimal parameters. Therefore, we propose a tuning-free method for automatically selecting the optimal robustification parameter with high efficiency. The simulation study demonstrates the more fabulous performance of our proposed method than traditional biclustering methods when encountering heavy-tailed noise. A real-life biomedical application is also presented. The R package RcvxBiclustr is available at https://github.com/YifanChen3/RcvxBiclustr.