Dual graph-regularized sparse concept factorization for clustering

The concept factorization algorithm has received widespread attention and achieved remarkable results in the field of clustering. However, when modeling this clustering algorithm, it is necessary to initialize two new low-dimensional matrices that are independent of the objective matrix and continuously approximate the objective matrix through alternating iterative updating, thus inevitably introducing some noise factors that are undesirable for the model. Especially in the objective function constructed by square loss, the noise factors have a more significant influence on the clustering performance. To solve this issue, a dual graph-regularized sparse concept factorization (DGSCF) algorithm is proposed in this paper. In addition to maintaining the geometric structure of the data using dual graph regularization, DGSCF adopts an optimization framework based on l(1) and Frobenius norms, which enhance the ability of feature selection and sparsity to eliminate the influence of noise factors on the algorithm performance. The corresponding alternating iterative updating rules and convergence proof of the DGSCF are provided. Finally, experiments on eight public datasets show its effectiveness and superiority. (c) 2022 Elsevier Inc. All rights reserved.