Subspace clustering based on alignment and graph embedding

摘要

In this paper, we propose a new subspace clustering method based on alignment and graph embedding (SCAGE). In SCAGE, we unify the image alignment process and clustering subspace learning process based on low rank and sparse representation. Besides, we use the label prediction information, error information and coefficients to conduct the graph embedding. In addition, the prior knowledge is used to initialize the label prediction matrix which not only speeds up the converge of the clustering process but also achieves a better result. Various experiments show that SCAGE achieves better performance than state-of-the-art algorithms.