Enhancing Unsupervised Feature Selection via Double Sparsity Constrained
Optimization
Enhancing Unsupervised Feature Selection via Double Sparsity Constrained
Optimization
Unsupervised feature selection (UFS) is widely applied in machine learning and pattern recognition. However, most of the existing methods only consider a single sparsity, which makes it difficult to select valuable and discriminative feature subsets from the original high-dimensional feature set. In this paper, we propose a new UFS method …