关键词: 子空间聚类, 相关系数, 数据相似度, 一致性

Abstract:

A new subspace clustering model Structured Weighted Correlation Adaptive Subspace Clustering（SWCASC） is proposed to solve the problem that the structural sparse subspace clustering cannot well grasp the consistency of data similarity. In this model, the correlation coefficient of data points is introduced to impose an explicit penalty on the representation coefficient, and the subspace structure norm is introduced for the dependency relation between segmentation and similarity. The new model makes the data category labels consistent, the similarity matrix sparse and consistent, and self-adaptive. The sparsity of similarity matrix is conducive to separating data from different subspaces, while consistency is conducive to aggregating data from the same subspace. Extensive experiments show that the method has better classification performance and is superior to other methods.

Key words: subspace clustering, correlation coefficient, data similarity, consistency