Self-supervised star graph optimization embedding non-negative matrix factorization

Labeling expensive and graph structure fuzziness are recognized as indispensable prerequisites for solving practical problems in semi-supervised graph learning. This paper proposes a novel approach: a non-negative matrix factorization algorithm based on self-supervised star graph optimal embedding, utilizing the progressive spontaneous strategy of anchor graphs. The model considers the feature assignment rules in unlabeled samples and constructs a corresponding probabilistic extension model to extract pseudo-labeled information from the samples. It also constructs self-supervised hard constraints accordingly to enhance the learning process. In addition, inspired by the graph structure filter, we propose a star graph optimization method. It smooths the association relationships between nodes in the graph structure and improves the accuracy of the graph regularization term in describing the association relationships of the original data. Finally, we give the objective function of the model with the multiplicative update rule and analyze the convergence of the algorithm under this rule. Clustering experiments on several standard image datasets and electroencephalography datasets show that the proposed algorithm improves over the current state-of-the-art benchmark algorithms by 6.9% on average. This indicates that the proposed model has excellent self-supervised label discovery and data representation capabilities.