Unsupervised Feature Selection Using Bayesian Tucker Decomposition

In this letter, we propose Bayesian Tucker decomposition (BTuD) in which the residuals are supposed to follow 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 data sets, 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 feature extraction (FE) is expected to coincide with TD-based unsupervised FE that were previously proposed and successfully applied to a wide range of problems.