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基于多视图矩阵分解的聚类分析

张祎 孔祥维 王振帆 付海燕 李明

张祎, 孔祥维, 王振帆, 付海燕, 李明. 基于多视图矩阵分解的聚类分析. 自动化学报, 2018, 44(12): 2160-2169. doi: 10.16383/j.aas.2018.c160636
引用本文: 张祎, 孔祥维, 王振帆, 付海燕, 李明. 基于多视图矩阵分解的聚类分析. 自动化学报, 2018, 44(12): 2160-2169. doi: 10.16383/j.aas.2018.c160636
ZHANG Yi, KONG Xiang-Wei, WANG Zhen-Fan, FU Hai-Yan, LI Ming. Matrix Factorization for Multi-view Clustering. ACTA AUTOMATICA SINICA, 2018, 44(12): 2160-2169. doi: 10.16383/j.aas.2018.c160636
Citation: ZHANG Yi, KONG Xiang-Wei, WANG Zhen-Fan, FU Hai-Yan, LI Ming. Matrix Factorization for Multi-view Clustering. ACTA AUTOMATICA SINICA, 2018, 44(12): 2160-2169. doi: 10.16383/j.aas.2018.c160636

基于多视图矩阵分解的聚类分析

doi: 10.16383/j.aas.2018.c160636
基金项目: 

国家自然科学基金创新研究群体科学基金 71421001

国家自然科学基金 61772111

详细信息
    作者简介:

    张祎  大连理工大学硕士研究生.主要研究方向为多媒体信息安全和计算机视觉.E-mail:yiz@mail.dlut.edu.cn

    王振帆  大连理工大学硕士研究生.主要研究方向为计算机视觉.E-mail:zfwang@mail.dlut.edu.cn

    付海燕  大连理工大学信息与通信工程学院副教授.2014年获得大连理工大学信息与信号处理博士学位.主要研究方向为图像检索和计算机视觉.E-mail:fuhy@dlut.edu.cn

    李明  大连理工大学信息与通信工程学院副教授.2010年获得纽约州立大学电子工程系博士学位.主要研究方向为通信理论和信号处理.E-mail:mli@dlut.edu.cn

    通讯作者:

    孔祥维  浙江大学数据科学与管理工程系教授.2003年获得管理科学与工程专业博士学位.2006~2007年美国普渡大学访问学者.主要研究方向为数字图像处理, 图像检索, 计算机视觉, 多媒体信息安全和数字媒体取证.本文通信作者.E-mail:kongxiangwei@zju.edu.cn

Matrix Factorization for Multi-view Clustering

Funds: 

the Foundation for Innovative Research Groups of the NSFC 71421001

National Natural Science Foundation of China 61772111

More Information
    Author Bio:

     Master student at the School of Information and Communication Engineering, Dalian University of Technology. Her research interest covers multimedia information security and computer vision

     Master student at the School of Information and Communication Engineering, Dalian University of Technology. His main research interest is computer vision

     Associate professor at the School of Information and Communication Engineering, Dalian University of Technology. She received her Ph. D. from Dalian University of Technology in 2014. Her research interest covers image retrieval and computer vision

     Associate professor at the School of Information and Communication Engineering, Dalian University of Technology, China. He received his Ph. D. in electrical engineering from the State University of New York, USA in 2010. His research interest covers communication theory and signal processing

    Corresponding author: KONG Xiang-Wei  Professor in the Department of Data Science and Engineering Management, Zhejiang University, China. She received her Ph. D. degree in management science and engineering, Dalian University of Technology, China, in 2003. From 2006 to 2007, she was a visiting researcher in the Department of Computer Science at Purdue University, USA. Her research interest covers digital image processing, image retrieval, computer vision, multimedia information security, and digital media forensics. Corresponding author of this paper
  • 摘要: 在计算机视觉和模式识别领域,随着多源信息越来越多,图像的描述方法也越来越丰富,多视图学习方法能更充分利用这种多源信息,进而提高聚类的准确率.因此,本文提出了两种基于多视图学习的方法:MultiGNMF和MultiGSemiNMF方法.该方法是在矩阵分解的基础之上,结合以往多视图学习的框架准则,并利用了样本的局部结构形成的.MultiGNMF和MultiGSemiNMF算法不仅能学习视图间的互补信息,同时能保持样本的空间结构.但是,MultiGNMF算法只适用于非负的特征矩阵.因此,考虑到SemiNMF算法相对于NMF算法具有更大的扩展性,结合多视图学习的框架,本文又提出了多视图学习的MultiGSemiNMF算法.实验结果证实了这两种方法有较好的性能.
    1)  本文责任编委 王立威
  • 图  1  在UCI Digit数据库中参数$\lambda$对本文算法的影响

    Fig.  1  The influences of $\lambda$ on UCI Digit database

    图  2  在CMU PIE数据库中参数$\lambda$对本文算法的影响

    Fig.  2  The influences of $\lambda$ on CMU PIE database

    图  3  在UCI Digit中参数$k$对本文提出算法的影响

    Fig.  3  The influences of $k$ on UCI Digit database

    表  1  4个数据库的资料

    Table  1  The information of four databases

    数据库 数量 视图个数 聚类个数
    CMU PIE 2 856 2 68
    UCI Digit 2 000 2 10
    3-Sources 169 3 6
    ORL 400 2 40
    下载: 导出CSV

    表  2  不同方法在4个数据库中的AC值

    Table  2  The AC values by different methods in four databases

    算法 UCI Digit CMU PIE 3-Sources ORL
    BSV ${68.5\pm.05}$ ${55.2\pm.02}$ ${60.8\pm.01}$ ${51.8\pm.02}$
    WSV ${63.4\pm.04}$ ${47.6\pm.01}$ ${49.1\pm.03}$ ${42.8\pm.05}$
    ConcatNMF ${67.8\pm.06}$ ${51.5\pm.00}$ ${58.6\pm.03}$ ${66.8\pm.02}$
    ColNMF ${66.0\pm.05}$ ${56.3\pm.00}$ ${61.3\pm.02}$ ${66.3\pm.04}$
    Co-reguSC ${86.6\pm.00}$ ${59.5\pm.02}$ ${47.8\pm.01}$ ${70.5\pm.02}$
    MultiNMF ${88.1\pm.01}$ ${64.8\pm.02}$ ${68.4\pm.06}$ ${67.3\pm.03}$
    SC-ML ${88.1\pm.00}$ ${72.3\pm.00}$ ${54.0\pm.00}$ ${70.8\pm.00}$
    MMSC ${89.4\pm.38}$ ${61.6\pm.06}$ ${69.9\pm.04}$ ${70.7\pm.06}$
    AMGL ${82.6\pm.74}$ ${63.6\pm.19}$ ${68.3\pm.12}$ ${64.2\pm.10}$
    MultiGNMF ${95.1\pm.10}$ ${72.5\pm.02}$ ${73.4\pm.00}$ ${73.5\pm.00}$
    MultiGSemiNMF ${95.6\pm.02}$ ${77.2\pm.12}$ ${79.3\pm.00}$ ${76.2\pm.00}$
    下载: 导出CSV

    表  3  不同方法在4个数据库中的NMI值

    Table  3  The NMI values by different methods in four databases

    算法 UCI Digit CMU PIE 3-Sources ORL
    BSV ${63.4\pm.03}$ ${74.1\pm.00}$ ${53.0\pm.01}$ ${69.8\pm.01}$
    WSV ${60.3\pm.03}$ ${69.1\pm.02}$ ${44.1\pm.02}$ ${65.4\pm.04}$
    ConcatNMF ${60.3\pm.03}$ ${70.5\pm.00}$ ${51.7\pm.03}$ ${85.1\pm.01}$
    ColNMF ${62.1\pm.03}$ ${68.3\pm.00}$ ${55.2\pm.02}$ ${84.5\pm.03}$
    Co-reguSC ${77.0\pm.00}$ ${80.5\pm.01}$ ${41.4\pm.01}$ ${80.5\pm.01}$
    MultiNMF ${80.4\pm.01}$ ${82.2\pm.02}$ ${60.2\pm.06}$ ${87.6\pm.01}$
    SC-ML ${87.6\pm.00}$ ${85.1\pm.00}$ ${45.5\pm.00}$ ${85.3\pm.00}$
    MMSC ${88.0\pm.05}$ ${80.3\pm.01}$ ${66.3\pm.09}$ ${85.9\pm.01}$
    AMGL ${84.9\pm.33}$ ${81.8\pm.10}$ ${56.9\pm.11}$ ${81.9\pm.02}$
    MultiGNMF ${90.1\pm.04}$ ${90.2\pm.01}$ ${65.4\pm.00}$ ${88.5\pm.00}$
    MultiGSemiNMF ${91.2\pm.01}$ ${93.8\pm.05}$ ${73.4\pm.00}$ ${89.9\pm.00}$
    下载: 导出CSV
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  • 收稿日期:  2016-09-07
  • 录用日期:  2017-08-17
  • 刊出日期:  2018-12-20

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