With the explosion of unlabelled and high-dimensional data, unsupervised feature selection has become an critical and challenging problem in machine learning. Recently, data representation based model has been successfully deployed for unsupervised feature selection, which defines feature importance as the capability to represent original data via a reconstruction function. However, most existing algorithms conduct feature selection on original feature space, which will be affected by the noisy and redundant features of original feature space. In this paper, we investigate how to conduct feature selection on the dictionary basis space of the data, which can capture higher level and more abstract representation than original low-level representation. In addition, a similarity graph is learned simultaneously to preserve the local geometrical data structure which has been confirmed critical for unsupervised feature selection. In summary, we propose a model (referred to as DGL-UFS briefly) to integrate dictionary learning, similarity graph learning and feature selection into a uniform framework. Experiments on various types of real world datasets demonstrate the effectiveness of the proposed framework DGL-UFS.

随着未标记和高维数据的爆炸式增长，无监督特征选择已成为机器学习中的关键和挑战性问题。最近，基于数据表示的模型已成功部署用于无监督的特征选择，该模型将特征重要性定义为通过重建函数表示原始数据的能力。然而，大多数现有算法在原始特征空间上进行特征选择，这将受到原始特征空间的噪声和冗余特征的影响。在本文中，我们研究了如何在数据的字典基础空间上进行特征选择，与原始的低级表示相比，该功能可以捕获更高级别和更抽象的表示。此外，同时学习相似图以保留局部几何数据结构，该结构已被证实对于无监督特征选择至关重要。总之，我们提出了一个模型（简称为DGL-UFS）将字典学习，相似图学习和特征选择集成到一个统一的框架中。在各种类型的现实世界数据集上的实验证明了所提出的框架DGL-UFS的有效性。