Robust Bayesian Subspace Identification for Small Data Sets

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 est...

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Bibliographic Details
Main Author Mesquita, Alexandre Rodrigues
Format Journal Article
LanguageEnglish
Published 28.12.2022
Subjects
Computer Science - Systems and Control
Statistics - Machine Learning
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Summary: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.
DOI:10.48550/arxiv.2212.14132