Hao Zheng

In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases.

To optimize the data clustering effect of public health emergencies, an application research on social governance ability under public health emergencies based on artificial intelligence is proposed. First, the firefly optimization algorithm is used to collect the information data of the social governance ability of public health emergencies, establish a unified format, and save it. Then, artificial intelligence technology is used to mine the correlation of clustering data, and on this basis, a learning model integrating global structure information and local structure information is established. Finally, the social governance model under public health emergencies is established. The experimental results show that the design method has high clustering accuracy, regularization cross index, and adjusted rand index (ARI) index. This shows that the design method can improve the social governance ability of data fusion clustering and improve the social governance ability.