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：本文提出贝叶斯Tucker分解（BTuD），其中残差被假设为服从类似于线性回归的高斯分布。尽管我们已提出执行所提BTuD的算法，但传统的高阶正交迭代可生成与当前实现一致的Tucker分解。利用所提BTuD，我们能够成功对多种合成数据集、随机耦合强度的全局耦合映射及基因表达谱进行无监督特征选择。因此可以得出结论：我们新提出的无监督特征选择方法具有良好前景。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TD的无监督特征提取方法一致。