稀疏条件下的重叠子空间聚类算法

he existing subspace clustering algorithms cannot balance the density of the data in the same subspace and the sparsity of the data between different subspaces and most algorithms cannot solve the overlap of data. To solve the above problems, this paper proposed a novel algorithm of overlapping subspace clustering algorithm under sparse condition (OSCSC) . The algorithm used the mixed norm representation method of L1 norm and Frobenius norm to establish the subspace representation model, and the weighted L1 norm regular term could improve the sparsity of different subspaces and the density of the same subspace. Then, the algorithm performed rechecks on the partitioned subspaces by using an overlapping probability model subject to exponential family distribution to determine whether exist overlapping in different subspaces, which could further improve the accuracy of clustering. The results of the experiment on both artificial datasets and real-world datasets show that the algorithm has better clustering performance by being compared to other contrast algorithms.