Deep Semi-Supervised Learning via Dynamic Anchor Graph Embedding Learning

Recently, deep semi-supervised graph embedding learning has shown much promise on text and image recognition tasks when the number of labeled data is limited. By introducing an auxiliary unsupervised task of predicting the neighborhood context in the graph, these approaches effectively mine the structure information provided by abundant unlabeled data. However, existing methods usually adapt to datasets whose graph is explicitly given or predefined, which cannot handle large-scale datasets with unknown graphs. Besides, the edge connections and weights are fixed during the training process in these methods, which fails to use the current feature information extracted by the model. In this paper, we propose a novel deep semi-supervised dynamic anchor graph embedding learning algorithm. More specifically, we build a two-branch architecture to learn the single-sample local features and the global features in the graph simultaneously. The first branch constrains its outputs to be consistent with different perturbations of the same single sample. And the output features are dedicated to constructing the dynamic anchor graph. The second branch utilizes the graph and the model prediction to sample the context. Then the global graph embeddings are learned based on the context. Finally, the outputs of the two branches are aggregated to jointly predict the class labels. Extensive experimental results on several image and text datasets have shown that the proposed method is able to improve the performance of existing graph embedding learning methods and outperform many state-of-the-art methods on image classification.