In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regression. Although we have proposed an algorithm to perform the proposed BTuD, the conventional higher-order orthogonal iteration can generate Tucker decomposition consistent with the present implementation. Using the proposed BTuD, we can perform unsupervised feature selection successfully applied to various synthetic datasets, global coupled maps with randomized coupling strength, and gene expression profiles. Thus we can conclude that our newly proposed unsupervised feature selection method is promising. In addition to this, BTuD based unsupervised FE is expected to coincide with TD based unsupervised FE that were previously proposed and successfully applied to a wide range of problems.

翻译：本文提出了贝叶斯塔克分解（BTuD），其中残差被假定服从类似于线性回归的高斯分布。尽管我们已提出一种算法来实现所提出的BTuD，但传统的高阶正交迭代方法可以生成与当前实现一致的塔克分解。利用所提出的BTuD，我们能够成功地对各种合成数据集、具有随机耦合强度的全局耦合映射以及基因表达谱进行无监督特征选择。因此，我们得出结论，我们新提出的无监督特征选择方法具有应用前景。此外，基于BTuD的无监督特征提取有望与先前提出并成功应用于广泛问题的基于塔克分解的无监督特征提取方法相吻合。