# Robust Bayesian Subspace Identification for Small Data Sets

arXiv: https://arxiv.org/abs/2212.14132 · HTML: https://mlsanity.com/paper/2212.14132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Finite Sample Analysis of Stochastic System Identification](https://mlsanity.com/paper/1903.09122/) (2019)
- [Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint](https://mlsanity.com/paper/1507.00564/) (2015)
- [Equivariant and scale-free Tucker decomposition models](https://mlsanity.com/paper/1312.6397/) (2013)
- [Regularization and Bayesian Learning in Dynamical Systems: Past, Present and Future](https://mlsanity.com/paper/1511.01543/) (2015)
- [Hyperspectral Subspace Identification Using SURE](https://mlsanity.com/paper/1606.00219/) (2016)
- [The Optimal Hard Threshold for Singular Values is 4/sqrt(3)](https://mlsanity.com/paper/1305.5870/) (2013)

## Abstract (arXiv)

Model estimates obtained from traditional subspace identification methods may be subject to significant variance. This elevated variance is aggravated in the cases of large models or of a limited sample size. Common solutions to reduce the effect of variance are regularized estimators, shrinkage estimators and Bayesian estimation. In the current work we investigate the latter two solutions, which have not yet been applied to subspace identification. Our experimental results show that our proposed estimators may reduce the estimation risk up to 40% of that of traditional subspace methods.