计算机工程与应用2017，Vol.53Issue(24)：23-29,7.DOI:10.3778/j.issn.1002-8331.1711-0049
结构化稀疏低秩子空间聚类
Structured sparse and low rank subspace clustering
摘要
Abstract
A new subspace structured low rank regularity is defined. Combining with the subspace Structured Sparse Sub-space Clustering method(SSSC), a new unified optimization model is given. The new model uses the estimated cluster membership of the samples and the affinity of the samples to guide each other so that the affinity possesses both discrimi-nation and coherence and the cluster membership have coherence property. The discrimination of the affinity tends to seg-ment data from different subspaces into different clusters while the coherence tends to group data from the same subspace into the same cluster. Experiments show that the proposed method outperforms the state-of-the-art two-stage methods and the SSSC method.关键词
子空间聚类/子空间结构化低秩/相似度/判别性/一致性Key words
subspace clustering/subspace structured low rank/affinity/discrimination/coherence分类
信息技术与安全科学引用本文复制引用
张红,王卫卫,孔胜江..结构化稀疏低秩子空间聚类[J].计算机工程与应用,2017,53(24):23-29,7.基金项目
国家自然科学基金(No.61472303,No.61772389,No.61271294). （No.61472303,No.61772389,No.61271294）
