Subspace clustering analysis algorithms are often employed when dealing with high-dimensional data. As a representative approach, Low-Rank Representation (LRR) of data has achieved great success for subspace segmentation tasks in applications such as image processing. The traditional LRR-related methods consist of two separate tasks: first, the affinity graph construction by using lowrank minimization techniques, and then the spectral clustering, which is done on the affinity graph to get the final segmentation. Since these two steps are independent of each other, this method does not guarantee that the results obtained by the algorithm are globally optimal. In this paper, a method called Robust Structured Low-Rank Representation (RSLRR) is proposed, by integrating the two above mentioned tasks and solve a joint optimization problem. This paper also puts forward a method to solve the joint optimization problem, which can efficiently get both the segmentation and the structured low-rank representation. Experiments on several standard datasets show that, compared with other algorithms, the algorithm proposed in this paper can achieve better clustering results.
Spectral clustering steered low-rank representation for subspace segmentation