Unsupervised Feature Selection with Adaptive Graph Embedding and Non- Convex Regular Feature Self-Expression

To solve the problem that the traditional unsupervised feature selection cannot fully take into account the local structure of samples and features, and does not consider that the non-convex regular term brings sparse solutions and can select more discriminant features, an unsupervised feature selection method based on adaptive graph embedding and non-convex regular feature self-expression is proposed. Firstly, the dimensions of features are reduced by the graph embedding technology, and a sample similarity matrix is obtained to guide feature selection. Secondly, the feature self-expression strategy is introduced to represent each feature linearly by the other features, and the similarity relationship between features is considered to maintain the local structure of features. Thirdly, a non-convex regular term is added to the feature self-expression to obtain a more sparse weight matrix for feature selection. In the process of feature selection, the adaptive graph embedding technology is performed to learn the local structure of data and select the optimal feature subset. Finally, to solve non-convex sparse problems, a new unsupervised feature selection algorithm is designed by using the alternate iterative method to optimize this solution model. Compared with other methods on six datasets, the experimental results show that the proposed method is effective. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.