Unsupervised feature selection using Bayesian Tucker decomposition
This work offers a new probabilistic approach to unsupervised feature selection, but its advantage over existing Tucker decomposition methods is unclear and the results are preliminary.
The authors propose Bayesian Tucker decomposition (BTuD) for unsupervised feature selection, demonstrating its effectiveness on synthetic datasets, global coupled maps, and gene expression profiles.
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.