Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864559 | Neurocomputing | 2018 | 13 Pages |
Abstract
High dimension is one of the key characters of big data. Feature selection, as a framework to identify a small subset of illustrative and discriminative features, has been proved as a basic solution in dealing with high-dimensional data. In previous literatures, â2, p-norm regularization was studied by many researches as an effective approach to select features across data sets with sparsity. However, â2, p-norm loss function is just robust to noise but not considering the influence of outliers. In this paper, we propose a new robust and efficient feature selection method with emphasizing Simultaneous Capped â2-norm loss and â2, p-norm regularizer Minimization (SCM). The capped â2-norm based loss function can effectively eliminate the influence of noise and outliers in regression and the â2, p-norm regularization is used to select features across data sets with joint sparsity. An efficient approach is then introduced with proved convergence. Extensive experimental studies on synthetic and real-world datasets demonstrate the effectiveness of our method in comparison with other popular feature selection methods.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Gongmin Lan, Chenping Hou, Feiping Nie, Tingjin Luo, Dongyun Yi,