Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also considers the underlying structure of data based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art methods.

特征选择方法对数据的可读性和降低学习算法的复杂性具有重要作用。近年来，由于对大型数据集的标注工作繁琐，因此基于无监督的观点对特征选择问题进行了各种研究。在本文中，我们提出了一种从子空间聚类开始的无监督特征选择的新方法，以通过样本之间低维子空间的表示学习来保持相似性。使用自表达模型以自适应方式隐式学习聚类相似性。所提出的方法不仅通过子空间聚类保持样本相似性，而且还基于正则化回归模型考虑了数据的底层结构。