Multi-level Low-rank Approximation-based Spectral Clustering for image segmentation

Ordered Subspace Clustering With Block-Diagonal Priors

Many application scenarios involve sequential data, but most existing clustering methods do not well utilize the order information embedded in sequential data. In this paper, we study the subspace clustering problem for sequential data and propose a new clustering method, namely ordered sparse clustering with block-diagonal prior (BD-OSC). Instead of using the sparse normalizer in existing sparse subspace clustering methods, a quadratic normalizer for the data sparse representation is adopted to model the correlation among the data sparse coefficients. Additionally, a block-diagonal prior for the spectral clustering affinity matrix is integrated with the model to improve clustering accuracy. To solve the proposed BD-OSC model, which is a complex optimization problem with quadratic normalizer and block-diagonal prior constraint, an efficient algorithm is proposed. We test the proposed clustering method on several types of databases, such as synthetic subspace data set, human face database, video scene clips, motion tracks, and dynamic 3-D face expression sequences. The experiments show that the proposed method outperforms state-of-the-art subspace clustering methods.