Dual-graph regularized sparse robust adaptive concept factorization

Due to the inability of traditional concept factorization methods to fully capture the intricate local and global manifold structures within the raw data space, they are unable to obtain detailed structural information effectively. To address this limitation, we put forward a concept factorization approach named sparse dual-graph regularized concept factorization with stable adaptive spectral clustering (SDCFSAS). Primarily, SDCFSAS leverages Dot-Product Weighting and stable adaptive spectral clustering to construct a similarity matrix that learns intrinsic features of the data, especially nonlinear or non-convex structures. Besides, by utilizing a robust estimator to filter the side effects of outlier points, it ensures that normal samples play a pivotal function in the construction of the model, enhancing the robustness and reliability of the model. Furthermore, the introduction of L-2,L-r-norm (1 <= r <= 2) taking for a measure on the deviation term further strengthens the robustness. Additionally, the computable sparse L-2,L-p-norm (0 < p <= 1) regularization terms are employed to establish a sparse model, improving the model's generalization capability, computational efficiency, and noise reduction. Finally, The performance of algorithm used to solve SDCFSAS is studied in detail, especially its convergence and computational complexity. To demonstrate the clustering performance and recognition ability of our SDCFSAS, we proceed comparative experiments on eight real-world datasets against other similar state-of-the-art algorithms. Moreover, statistical analysis is employed to validate the results, which showcase the significant advantages of our approach.