Semi-Supervised Structured Sparse Graph Data Classification
Shuai Shao, Mingze Tang · 2019
Face classification has been developed for many years with the aim of dividing the face image into the identity of the person to whom they belong. The existing classification methods still have some problems such as the classifier is difficult to conform to the multi-level graph model and the data in the real scene rarely has a valid label. In this paper, a semi-supervised structured sparse graph data classification method for face feature extraction is proposed. The method of transforming graph data into uniform length vector is used to construct a semi-supervised method using the manifold that preserves the global data structure and local data structure. Sparse graph data classifier was utilized in which sparse learning automatically obtains the connection relationship between the label data vector and the unlabeled data vector and its weight with the L2 norm to control the model. Finally it realizes the sparse representation of the semi-supervised data. Experiments based on standard face datasets show that this semi-supervised sparse graph data classification method has better classification performance.