Anchor‐Based Adaptive Similarity Graph Learning for Semi‐Supervised Classification

ABSTRACT Recently, graph semi‐supervised classification for different datasets is faced with some problems, such as low classification accuracy, and error labels in labeled data. To alleviate these problems, we propose a model called anchor‐based adaptive similarity graph learning for semi‐supervised classification (AAGSSL). This model leverages anchors to construct weight matrix associated with anchor and data, and obtains the sparse initial affinity graph by special matrix factorization. It adaptively learns a new similarity graph close to the initial affinity graph, which reduces the model's reliance of classification accuracy on the initial affinity graph. The model enhances the tolerance of error labels in the labeled data and accelerates the process of obtaining predictive labels by adjusting corresponding matrix internal parameters which introduced in model and employing label propagation, respectively. The feasibility and effectiveness of the model were verified in experiments on artificial datasets. Focus on image datasets classification, the comparative experiments on real benchmark image datasets verified the advantages of the proposed model in handling error labels and finding new class. And we additionally evaluate the impact of several parameters on classification performance and choose the best hyperparameters.