Semi-supervised Corrupted Face Classification via Graph Learning

Semi-supervised learning aims to training model with both of labeled and unlabeled data by exploring the relationships among them. Graph-based semi-supervised learning is an classical representative method that learning the class indicator matrix by propagating the similarity within the well designed graph constructed by data. However, for face data, they often happen to pixel missing or occlusion, which will degrade the graph learning performance, leading awful semi-supervised classification results. To address this problem, a novel semi-supervised corrupted face classification method via graph learning is proposed, in which the dynamic graph is learned by the completion face data recovered from the low-rank subspace. In our proposed method, the robust data representation and graph learning are implemented alternatively to obtain the overall optimal solutions. Experimental results demonstrate that our proposed method outperforms comparison methods on both of classification accuracy and robustness.