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Double-Structured Sparsity Guided Flexible Embedding Learning for Unsupervised Feature Selection
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DOI：10.1109/TNNLS.2023.3267184.png)
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Abstract
En 中文
In this article, we propose a novel unsupervised feature selection model combined with clustering, named double-structured sparsity guided flexible embedding learning (DSFEL) for unsupervised feature selection. DSFEL includes a module for learning a block-diagonal structural sparse graph that represents the clustering structure and another module for learning a completely row-sparse projection matrix using the l(2,0)-norm constraint to select distinctive features. Compared with the commonly used l(2,1)-norm regularization term, the l(2,0)-norm constraint can avoid the drawbacks of sparsity limitation and parameter tuning. The optimization of the l(2,0)-norm constraint problem, which is a nonconvex and nonsmooth problem, is a formidable challenge, and previous optimization algorithms have only been able to provide approximate solutions. In order to address this issue, this article proposes an efficient optimization strategy that yields a closed-form solution. Eventually, through comprehensive experimentation on nine real-world datasets, it is demonstrated that the proposed method outperforms existing state-of-the-art unsupervised feature selection methods.
Keywords:
l(2,0)-norm constraint optimization
block-diagonal structural sparse graph learning
structural row-sparsity subspace learning
unsupervised feature selection
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