论文标题
联合自适应图和结构性稀疏正规化，用于无监督特征选择
Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection
论文作者
论文摘要
特征选择是数据挖掘和机器学习中的重要数据预处理，可用于降低特征维度而不会降低模型的性能。由于获得带注释的数据在许多情况下都是费力的甚至是不可行的，因此无监督的特征选择在现实中更为实用。尽管已经提出了许多无监督特征选择的方法，但这些方法独立选择特征，因此不能保证所选特征组是最佳的。此外，必须仔细调整选定功能的数量以获得令人满意的结果。为了解决这些问题，我们提出了一个联合自适应图和结构化的稀疏性正规化无监督的特征选择（JASFS）方法，其中$ l_ {2,0} $ - 关于转换矩阵的规范正则化术语在转换矩阵中施加在形式上的学习中，用于特征选择，并且将图形的术语纳入了学习模型中，以将学习模型纳入了学习模型。一种有效而简单的迭代算法旨在通过分析计算复杂性来解决提出的优化问题。优化后，将在组中选择最佳特征的子集，并将自动确定所选功能的数量。与几种最新方法相比，八个基准的实验结果证明了该方法的有效性和效率。
Feature selection is an important data preprocessing in data mining and machine learning which can be used to reduce the feature dimension without deteriorating model's performance. Since obtaining annotated data is laborious or even infeasible in many cases, unsupervised feature selection is more practical in reality. Though lots of methods for unsupervised feature selection have been proposed, these methods select features independently, thus it is no guarantee that the group of selected features is optimal. What's more, the number of selected features must be tuned carefully to obtain a satisfactory result. To tackle these problems, we propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method in this paper, in which a $l_{2,0}$-norm regularization term with respect to transformation matrix is imposed in the manifold learning for feature selection, and a graph regularization term is incorporated into the learning model to learn the local geometric structure of data adaptively. An efficient and simple iterative algorithm is designed to solve the proposed optimization problem with the analysis of computational complexity. After optimized, a subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method compared with several state-of-the-art approaches.
