Unsupervised feature selection using Bayesian Tucker decomposition

TL;DR

This paper introduces Bayesian Tucker decomposition (BTuD), a novel unsupervised feature selection method that models residuals with Gaussian distribution, demonstrating successful application across diverse datasets.

Contribution

The paper proposes BTuD, a new Bayesian tensor decomposition method for unsupervised feature selection, aligning with existing Tucker decomposition algorithms.

Findings

Successfully applied to synthetic datasets, gene expression profiles, and coupled maps.

BTuD-based feature selection shows promising results across various applications.

Aligns with previously successful tensor decomposition-based feature selection methods.

Abstract

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.