智能系统学报2016，Vol.11Issue(5)：696-702,7.DOI:10.11992/tis.201601005
稀疏样本自表达子空间聚类算法
Sparse sample self-representation for subspace clustering
摘要
Abstract
Existing subspace clustering methods do not combine sample self⁃representation well with affinity matrix sparsity, for example, by removing disturbances from noise, outliers, etc., when constructing the affinity matrix. This paper proposes a novel subspace clustering method called sparse sample self⁃representation for subspace cluste⁃ring. This method fully considers the correlation between the samples, and also takes advantage of L1⁃norm and L2,1⁃norm terms to “penalize” the affinity matrix;that is, it conducts sparse sample self⁃representation for all test samples, to guarantee every sample can be expressed by any other samples with strong similarity and make it more robust. The experimental results of the Hopkins155 dataset and some facial image datasets show that the proposed method outperforms the LSR, SSC, and LRR methods in terms of the subspace clustering error.关键词
子空间聚类/谱聚类/子空间结构/相似度矩阵/样本自表达Key words
subspace clustering/spectral clustering/subspace structure/similarity matrix/sample self-representa-tion分类
信息技术与安全科学引用本文复制引用
林大华,杨利锋,邓振云,李永钢,罗..稀疏样本自表达子空间聚类算法[J].智能系统学报,2016,11(5):696-702,7.基金项目
国家自然科学基金项目（61263035，61573270，61450001）；国家973计划项目（2013CB329404）；中国博士后科学基金项目（2015M570837）；广西自然科学基金项目（2015GX NSFCB139011）；广西研究生教育创新计划项目（ YC-SZ2016046， YCSZ2016045）． （）
