基于图滤波与自表示的无监督特征选择算法

Unsupervised Feature Selection Algorithm Based on Graph Filtering and Self-representation

针对现有方法未考虑数据的高阶邻域信息而不能完全捕捉数据内在结构的问题,提出一种基于图滤波与自表示的无监督特征选择算法.首先,将高阶图滤波器应用于数据获得其平滑表示,并设计一个正则化器联合高阶图信息进行自表示矩阵学习以捕捉数据的内在结构;其次,应用l_(2,1)范数重建误差项和特征选择矩阵,以增强模型的鲁棒性与稀疏性选择判别的特征;最后,用一个迭代算法有效地求解所提出的目标函数,并进行仿真实验以验证该算法的有效性.

Aiming at the problem that existing methods could not fully capture the intrinsic structure of data without considering the higher-order neighborhood information of the data,we proposed an unsupervised feature selection algorithm based on graph filtering and self-representation.Firstly,a higher-order graph filter was applied to the data to obtain its smooth representation,and a regularizer was designed to combine the higher-order graph information for the self-representation matrix learning to capture the intrinsic structure of the data.Secondly,l_(2,1)norm was used to reconstruct the error term and feature selection matrix to enhance the robustness and row sparsity of the model to select the discriminant features.Finally,an iterative algorithm was applied to effectively solve the proposed objective function and simulation experiments were carried out to verify the effectiveness of the proposed algorithm.