# Low-rank matrix recovery with Ky Fan 2-k-norm

arXiv: https://arxiv.org/abs/1904.05590 · HTML: https://mlsanity.com/paper/1904.05590/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Non-geodesically-convex optimization in the Wasserstein space](https://mlsanity.com/paper/2406.00502/) (2024)
- [Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning](https://mlsanity.com/paper/2501.06884/) (2025)
- [Low-Rank Tensor Completion Based on Fractional Regularization with Ky Fan p-k Norm](https://mlsanity.com/paper/2606.19046/) (2026)

## Abstract (arXiv)

We propose Ky Fan 2-k-norm-based models for the nonconvex low-rank matrix recovery problem. A general difference of convex algorithm (DCA) is developed to solve these models. Numerical results show that the proposed models achieve high recoverability rates.