论文标题
小型数据集的强大贝叶斯子空间识别
Robust Bayesian Subspace Identification for Small Data Sets
论文作者
论文摘要
从传统的子空间识别方法获得的模型估计可能会有显着差异。在大型模型或样本量有限的情况下，这种升高的方差会加剧。降低方差影响的常见解决方案是正规估计器，收缩估计器和贝叶斯估计。在当前的工作中，我们研究了后两种解决方案，这些解决方案尚未应用于子空间识别。我们的实验结果表明，我们提出的估计器可能会将估计的风险降低到传统子空间方法的$ 40 \％$。
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.
