Unsupervised feature selection using Bayesian Tucker decomposition
Y-h. Taguchi, Yoh-ichi Mototake
TLDR
This paper introduces Bayesian Tucker decomposition (BTuD) for unsupervised feature selection, showing promising results across diverse datasets.
Key contributions
- Proposes Bayesian Tucker decomposition (BTuD) with Gaussian distributed residuals.
- Successfully performs unsupervised feature selection using the BTuD method.
- Validated on synthetic datasets, global coupled maps, and gene expression profiles.
- Shows consistency with conventional Tucker decomposition for feature extraction.
Why it matters
Unsupervised feature selection is crucial for handling complex datasets. This paper introduces a promising new Bayesian Tucker decomposition method that effectively identifies key features. Its successful application across diverse data types highlights its potential for real-world problems.
Original 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.
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