Subspace Clustering via Integrating Sparse Representation and Adaptive Graph Learning

Sparse representation is a powerful tool for subspace clustering, but most existing methods for this issue ignore the local manifold information in learning procedure. To this end, in this paper we propose a novel model, dubbed Sparse Representation with Adaptive Graph (SRAG), which integrates adaptive graph learning and sparse representation into a unified framework. Specifically, the former can preserve the local manifold structure of data, while the latter is useful for digging global information. For the objective function of SRAG has multiple intractable terms, an ADMM method is developed to solve it. Numerous experimental results demonstrate that our proposed method consistently outperforms several representative clustering algorithms by significant margins.