Abstract:An efficient spectral clustering algorithm based on subspace decomposition is proposed. Firstly, based on the matrix decomposition of consensus information and domain specific information, the linked documents are divided into three subspaces, the different effects of consensus information and domain specific information on clustering are modeled by adding regularization items to the subspaces, and the alternative optimization method is utilized to achieve spectral clustering. In addition, considering the complexity of spectral clustering, a gradient descent method with curvilinear search is proposed to accelerate the solution process. Experimental results on three real datasets show that the proposed algorithm is superior to the current typical baseline algorithm in terms of clustering quality and efficiency, and is insensitive to input parameters.