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J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:p> Multi-view feature selection is an important research direction in the field of multi-view learning. To remove the negative effect of irrelevant and redundant information and select the important features from the multi-view data, in this paper, we propose a novel hierarchical unsupervised multi-view feature selection method, which integrates matrix decomposition and hierarchical regularization as a joint model. Specifically, to exploit the consistency information of multiple views for projection matrix, we project the multiple views into a latent basis space based on a matrix decomposition model. The hierarchical regularization containing the [Formula: see text]-norm, dependence and Frobenius-norm is imposed on projection matrix to, respectively, exploit the row-level, dependency-level and view-level feature selection. 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