AI RESEARCH
Unsupervised feature selection using Bayesian Tucker decomposition
arXiv CS.LG
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ArXi:2604.14949v1 Announce Type: cross 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.