Robust and flexible multi-view subspace clustering with nuclear norm

Multi-view clustering technique utilizes the complementarity and consistency among different view features to divide the samples into different classes. Subspace learning garners considerable attention since it can explore the local structure in different dimensions. Although, multi-view subspace clustering algorithms have obtained remarkable performance, there are still some issues: 1) Nonlinear separable data sets cannot be exactly cut, which makes the flexibility be restricted; 2) The noise and outliers reduce the model robustness; 3) The clustering effectiveness is not outstanding. To solve these problems, this paper proposes a Robust and Flexible Multi-view Subspace Clustering with Nuclear Norm (RFMSC_NN), which integrates Multiple Kernel Learning (MKL) and Low-Rank Representation (LRR) within a cohesive framework. Specifically, firstly, projecting the linearly non-separable data to the Reproducing Kernel Hilbert Space (RKHS); Subsequently, learning a self-representation matrix to measure the similarity among samples; Then, by imposing the low rank constraint to reduce the noise interference; Next, adopting a self-weighted strategy to learn the weights of diverse views; Finally, using the k-means algorithm to obtain the clustering results. An alternate iteration optimization technique is employed to solve the model. Comprehensive experiments are conducted. The experimental results demonstrate enhanced clustering performance comparing with contemporary advanced multi-view clustering approaches.