特征自表达和图正则化的鲁棒无监督特征选择

为了在揭示数据全局结构的同时保留其局部结构，本文将特征自表达和图正则化统一到同一框架中，给出了一种新的无监督特征选择(unsupervised feature selection，UFS)模型与方法。模型使用特征自表达,用其余特征线性表示每一个特征，以保持特征的局部结构；用基于 ${L_{2, 1}}$ 范数的图正则化项，在保留数据的局部几何结构的同时可以降低噪声数据对特征选择的影响；除此之外，在权重矩阵上施加了低秩约束，保留数据的全局结构。在6个不同的公开数据集上的实验表明，所给算法明显优于其他5个对比算法，表明了所提出的UFS框架的有效性。

Feature self-representation and graph regularization for robust unsupervised feature selection

In order to reveal the global structure of data and preserve its local structure, this paper proposes a new unsupervised feature selection (UFS) method, which puts feature self-representation and graph regularization into the same framework. Specifically, the model uses the self-representation of the features to represent each feature through other features for preserving the local structure of the features. An ${L_{2, 1}}$-norm based graph regularization term is used to reduce the effect of noisy data on feature selection while preserving the local geometric structure. Furthermore, the model uses a low-rank constraint on the weight matrix to preserve the global structure. Experiments on six different public datasets show that the algorithm is clearly superior to the other five algorithms, which demonstrates the effectiveness of the proposed UFS framework.