Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features is selected out for effective data representation. In this paper, we proposes a novel robust unsupervised features selection method based on graph-preserving feature selection embedding LLE. Specifically, we integrate the graph matrix learning and the low-dimensional space learning together to identify the correlation among both features and samples from the intrinsic low-dimensional space of original data. Also, the global and local correlation of features have been taken into consideration through the low-rank constraint and the feature-level representation property to find lower-dimensional representation which preserves not only the global and local correlation of features but also the global and local structure of training samples. Furthermore, we propose a new optimization algorithm to the resulting objective function, which iteratively updates the graph matrix and the intrinsic space in order to collaboratively improve each of them. Experimental analysis on 18 benchmark datasets verified that our proposed method outperformed the state-of-the-art feature selection methods in terms of classification and clustering performance.

中文翻译：

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一种新的图保留无监督特征选择嵌入具有低秩约束和特征级表示的 LLE

无监督特征选择是处理高维数据的强大工具，其中选择特征子集进行有效的数据表示。在本文中，我们提出了一种基于图形保留特征选择嵌入 LLE 的新型鲁棒无监督特征选择方法。具体来说，我们将图矩阵学习和低维空间学习结合在一起，从原始数据的内在低维空间中识别特征和样本之间的相关性。此外，通过低秩约束和特征级表示属性考虑了特征的全局和局部相关性，以寻找低维表示，不仅保留了特征的全局和局部相关性，还保留了全局和局部的相关性。训练样本的结构。此外，我们对最终的目标函数提出了一种新的优化算法，该算法迭代更新图矩阵和内在空间，以协同改进它们中的每一个。对 18 个基准数据集的实验分析证实，我们提出的方法在分类和聚类性能方面优于最先进的特征选择方法。