Original paper
Unsupervised feature selection with multi-subspace randomization and collaboration
Abstract
Unsupervised feature selection has been an important technique in high-dimensional data analysis. Despite significant success, most of the existing unsupervised feature selection methods tend to estimate the underlying structure of data in the original feature space, but lack the ability to explore various subspaces in the high-dimensional space. In this paper, we argue that the use of a large number of random subspaces can significantly benefit...
Paper Details
Title
Unsupervised feature selection with multi-subspace randomization and collaboration
Published Date
Oct 1, 2019
Journal
Volume
182
Pages
104856 - 104856
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Notes
History