Joint clustering and feature selection based on trace ratio model and coordinate descent method
摘要
Joint clustering and dimensionality reduction methods have great potential in solving high-dimensional problems. Feature extraction and feature selection are both feature reduction methods. Compared to feature extraction, feature selection retains the original features and has advantages in certain aspects. To achieve optimized clusters while selecting distinct features, this paper imposes the L2,0-norm on the discriminative projected matrix based on the trace difference model. To solve the joint optimization model, we use the coordinate descent method to solve for the discriminative projected matrix and the cluster indicator matrix alternately. Extensive experiments show that the proposed model improves accuracy, NMI, and purity compared to other related clustering models. The proposed algorithm also demonstrates good convergence and stability across different datasets.