[1]林大华,杨利锋,邓振云,等.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.[doi:10.11992/tis.201601005]
 LIN Dahua,YANG Lifeng,DENG Zhenyun,et al.Sparse sample self-representation for subspace clustering[J].CAAI Transactions on Intelligent Systems,2016,11(5):696-702.[doi:10.11992/tis.201601005]
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稀疏样本自表达子空间聚类算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年5期
页码:
696-702
栏目:
出版日期:
2016-11-01

文章信息/Info

Title:
Sparse sample self-representation for subspace clustering
作者:
林大华1 杨利锋2 邓振云2 李永钢2 罗䶮2
1. 广西电化教育馆, 广西 南宁 530022;
2. 广西师范大学 广西多源信息挖掘与安全重点实验室, 广西 桂林 541004
Author(s):
LIN Dahua1 YANG Lifeng2 DENG Zhenyun2 LI Yonggang2 LUO Yan2
1. Guangxi Center for Educational Technology, Nanning 530022, China;
2. Guangxi Key Lab of Multi-source Information Mining & Security, Guilin 541004, China
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
Keywords:
subspace clusteringspectral clusteringsubspace structuresimilarity matrixsample self-representation
分类号:
TP181
DOI:
10.11992/tis.201601005
摘要:
针对现有子空间聚类算法在构造相似度矩阵时,没有同时利用样本自表达和稀疏相似度矩阵以及去除噪音、离群点的干扰相结合,提出了一种新的稀疏样本自表达子空间聚类方法。该方法通过样本自表达而充分利用样本间固有相关性的本质,创新性地同时使用L1-范数和L2,1-范数正则化项惩罚相似度矩阵,即对所有测试样本进行稀疏样本自表达,从而确保每个测试样本由与其相关性强的样本表示,并使所获得的相似度矩阵具有良好的子空间结构和鲁棒性。通过Hopkins155和人脸图像等大量数据集的实验结果表明,本文方法在实际数据的子空间聚类中能够获得非常好的效果。
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 clustering. 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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-01-04。
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(2015GXNSFCB139011);广西研究生教育创新计划项目(YCSZ2016046,YCSZ2016045).
作者简介:林大华,男,1979年生,主要研究方向为机器学习、数据挖掘;杨利锋,男,1989年生,硕士研究生,主要研究方向为数据挖掘和机器学习;邓振云,男,1991年生,硕士研究生,主要研究方向为机器学习、数据挖掘。发表学术论文8篇,其中被SCI、EI检索4篇。
通讯作者:杨利锋.E-mail:517567113@qq.com
更新日期/Last Update: 1900-01-01