Structured Graph Optimization for Unsupervised Feature Selection
Abstract
Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully...
Paper Details
Title
Structured Graph Optimization for Unsupervised Feature Selection
Published Date
Jan 1, 2019
Pages
1 - 1
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