# Robust Subspace System Identification via Weighted Nuclear Norm Optimization

arXiv: https://arxiv.org/abs/1312.2132 · HTML: https://mlsanity.com/paper/1312.2132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2013.

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

- [Robust online joint state/input/parameter estimation of linear systems](https://mlsanity.com/paper/2204.05663/) (2022)

## Abstract (arXiv)

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.