# ℓ0 factor analysis

arXiv: https://arxiv.org/abs/2411.08468 · HTML: https://mlsanity.com/paper/2411.08468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Certifiably Optimal Low Rank Factor Analysis](https://mlsanity.com/paper/1604.06837/) (2016)
- [Sparse plus Low rank Network Identification: A Nonparametric Approach](https://mlsanity.com/paper/1510.02961/) (2015)

## Abstract (arXiv)

Factor Analysis is about finding a low-rank plus sparse additive decomposition from a noisy estimate of the signal covariance matrix. In order to get such a decomposition, we formulate an optimization problem using the nuclear norm for the low-rank component, the ℓ0 norm for the sparse component, and the Kullback-Leibler divergence to control the residual in the sample covariance matrix. An alternating minimization algorithm is designed for the solution of the optimization problem. The effectiveness of the algorithm is verified via simulations on synthetic and real datasets.