Nonnegative spectral clustering and adaptive graph-based matrix regression for unsupervised image feature selection

Matrix regression model can directly take matrix data as input data, and its loss function is defined by left and right regression matrices. The spectral clustering-based matrix regression model can perform feature selection for unsupervised images. However, the graph weight matrix used in the existing spectral clustering models is predefined, which is often inaccurate, especially for noisy images. Moreover, they do not consider the preservation of local structure of image samples in transformation space. To this end, we propose a nonnegative spectral clustering and adaptive graph-based matrix regression model for unsupervised image feature selection. This model can make the prediction label matrix as smooth as possible on the whole graph, and the graph weight matrix can be adaptively learned instead of being predefined as fixed matrix. Thus, the accurate local structure of the sample data is preserved in transformation space and the discriminative information of these pseudo class labels can be revealed. Finally, we devise an efficient optimization algorithm to solve the proposed problem and analyze the computational complexity and convergence of the algorithm. Some experimental results on several datasets also show the effectiveness and superiority of our proposed method.