基于图嵌入的正交局部保持投影无监督特征选择

The traditional unsupervised feature selection algorithm based on graph learning often adopts sparse regularization method. However, this approach relies too heavily on the efficiency of graph learning, and it is not easy to tune regularization parameters. To solve this problem, an unsupervised feature selection algorithm based on graph embedding learning with orthogonal locality preserving projection is proposed in this paper. Firstly, we utilize locality preserving projection method to enhance the linear mapping ability that can maintain the local geometric manifold structure of the data, and orthogonal projection mode brings convenience to data reconstruction. Moreover, we use graph embedding learning method to quickly learn the similarity matrix of data. Then, l_2.0-norm constrained projection matrix to select discriminative features. Finally, a new nonparametric algorithm is used to efficiently solve the model problem iteratively since l_2.0-norm belongs to NP problem. Experimental results prove the effectiveness and superiority of the proposed algorithm.