# On Low-Rank Hankel Matrix Denoising

arXiv: https://arxiv.org/abs/2012.07433 · HTML: https://mlsanity.com/paper/2012.07433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning](https://mlsanity.com/paper/2511.00814/) (2025)
- [Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation](https://mlsanity.com/paper/2604.26787/) (2026)

## Abstract (arXiv)

The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise-free matrix. In this paper, recent results in matrix denoising by singular value shrinkage are reviewed. A novel approach is proposed to solve the low-rank Hankel matrix denoising problem by using an iterative algorithm in structured low-rank approximation modified with data-driven singular value shrinkage. It is shown numerically in both the input-output trajectory denoising and the impulse response denoising problems, that the proposed method performs the best in terms of estimating the noise-free matrix among existing algorithms of low-rank matrix approximation and denoising.