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hopfliking铁杆木虫 (小有名气)
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[求助]
请帮忙查一下下面文章的检索信息，感谢
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an efficient matrix factorization based low-rank representation for subspace clustering [ 发自手机版 http://muchong.com/3g ] |
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muse
捐助贵宾 (知名作家)
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【答案】应助回帖
★ ★ ★ ★ ★
感谢参与，应助指数 +1
hopfliking: 金币+5, ★★★★★最佳答案 2014-09-21 19:17:37
sunshan4379: LS-EPI+1, 感谢应助！ 2014-09-21 19:37:04
感谢参与，应助指数 +1
hopfliking: 金币+5, ★★★★★最佳答案 2014-09-21 19:17:37
sunshan4379: LS-EPI+1, 感谢应助！ 2014-09-21 19:37:04
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An efficient matrix factorization based low-rank representation for subspace clustering 作者:Liu, YY (Liu, Yuanyuan)[ 1 ] ; Jiao, LC (Jiao, L. C.)[ 1 ] ; Shang, FH (Shang, Fanhua)[ 1 ] PATTERN RECOGNITION 卷: 46 期: 1 页: 284-292 DOI: 10.1016/j.patcog.2012.06.011 出版年: JAN 2013 查看期刊信息 摘要 In recent years, robust subspace clustering is an important unsupervised clustering problem in machine learning and computer vision communities. The recently proposed spectral clustering based approach, called low-rank representation (LRR), yields an optimal solution for the case of independent subspaces and partially corrupted data. However, it has to be solved iteratively and involves singular value decomposition (SVD) at each iteration, and then suffers from high computation cost of multiple SVDs. In this paper, we propose an efficient matrix tri-factorization (MTF) approach with a positive semidefinite (PSD) constraint to approximate the original nuclear norm minimization (NNM) problem and mitigate the computation cost of performing SVDs. Specially, we introduce a matrix tri-factorization idea into the original low-rank representation framework, and then convert it into a small scale matrix nuclear norm minimization problem. Finally, we establish an alternating direction method (ADM) based algorithm to efficiently solve the proposed problem. Experimental results on a variety of synthetic and real-world data sets validate the efficiency, robustness and effectiveness of the proposed MTF approach comparing with the state-of-the-art algorithms. (C) 2012 Elsevier Ltd. All rights reserved. 关键词 作者关键词:Nuclear norm minimization (NNM); Low rank representation; Alternating direction method (ADM); Matrix tri-factorization; Positive semidefinite (PSD) KeyWords Plus:MOTION SEGMENTATION; FACE RECOGNITION; ALGORITHM 作者信息 通讯作者地址: Liu, YY (通讯作者) [显示增强组织信息的名称] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Mailbox 224,2 S TaiBai Rd, Xian 710071, Peoples R China. 地址: [显示增强组织信息的名称] [ 1 ] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China 电子邮件地址:yuanyuanliu0917@yahoo.com.cn; jlcxidian@163.com; shangfanhua@hotmail.com 基金资助致谢 基金资助机构 授权号 National Natural Science Foundation of China 60971112 60971128 60970067 61072108 Fund for Foreign Scholars in University Research and Teaching Programs (111 Project) B07048 Fundamental Research Funds for the Central Universities JY10000902001 JY10000902041 JY10000902043 查看基金资助信息 出版商 ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 类别 / 分类 研究方向:Computer Science; Engineering Web of Science 类别:Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic 文献信息 文献类型:Article 语种:English 入藏号: WOS:000309785000024 ISSN: 0031-3203 期刊信息 目录： Current Contents Connect® Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: 020CA Web of Science 核心合集中的 "引用的参考文献": 42 Web of Science 核心合集中的 "被引频次": 2 |
2楼2014-09-21 19:13:51
muse
捐助贵宾 (知名作家)
- LS-EPI: 147
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3楼2014-09-21 19:14:07