IEEE Trans Cybern. 2022 Aug;52(8):8413-8424. doi: 10.1109/TCYB.2021.3060804. Epub 2022 Jul 19.
In big data time, selecting informative features has become an urgent need. However, due to the huge cost of obtaining enough labeled data for supervised tasks, researchers have turned their attention to semisupervised learning, which exploits both labeled and unlabeled data. In this article, we propose a sparse discriminative semisupervised feature selection (SDSSFS) method. In this method, the ϵ -dragging technique for the supervised task is extended to the semisupervised task, which is used to enlarge the distance between classes in order to obtain a discriminative solution. The flexible l norm is implicitly used as regularization in the new model. Therefore, we can obtain a more sparse solution by setting smaller p . An iterative method is proposed to simultaneously learn the regression coefficients and ϵ -dragging matrix and predicting the unknown class labels. Experimental results on ten real-world datasets show the superiority of our proposed method.
在大数据时代，选择有用的特征已成为当务之急。然而，由于监督任务获取足够的标记数据的成本很高，研究人员已经将注意力转向半监督学习，该学习方法同时利用有标记和无标记数据。在本文中，我们提出了一种稀疏判别式半监督特征选择（SDSSFS）方法。在该方法中，将针对监督任务的ϵ -拖动技术扩展到半监督任务，用于扩大类之间的距离，以获得判别解。在新模型中，隐式使用灵活的 l 范数作为正则化项。因此，通过设置较小的 p ，我们可以得到更稀疏的解。提出了一种迭代方法来同时学习回归系数和ϵ -拖动矩阵并预测未知的类标签。在十个真实数据集上的实验结果表明了我们提出的方法的优越性。
