# On Matrix Factorizations in Subspace Clustering

arXiv: https://arxiv.org/abs/2106.12016 · HTML: https://mlsanity.com/paper/2106.12016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data](https://mlsanity.com/paper/1509.02649/) (2015)
- [CUR Decompositions, Similarity Matrices, and Subspace Clustering](https://mlsanity.com/paper/1711.04178/) (2017)
- [An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection](https://mlsanity.com/paper/1505.00526/) (2015)

## Abstract (arXiv)

This article explores subspace clustering algorithms using CUR decompositions, and examines the effect of various hyperparameters in these algorithms on clustering performance on two real-world benchmark datasets, the Hopkins155 motion segmentation dataset and the Yale face dataset. Extensive experiments are done for a variety of sampling methods and oversampling parameters for these datasets, and some guidelines for parameter choices are given for practical applications.