Object-attribute subspace with sparse feature edges detection
具有稀疏特征的对象—属性子空间边缘重叠区域归属算法

The overlapped regions among the identified objects-attributes subspaces by the traditional algorithm could influence the independence of these subspaces. In order to solve this defect, this paper developed the objects-attributes subspace edges detection algorithmOASEDAbased on K-means. It designed the objective function of edge detection, algorithm with the information of within-cluster and between-cluster, and optimized the objective function by the weight theory. In the end, experimental results on synthetic datasets demonstrate that the accuracy of the proposed algorithm.