Distribution Preserving Deep Semi-Nonnegative Matrix Factorization

被引:2
作者
Tan, Zhuolin [1 ,2 ]
Qin, Anyong [1 ,2 ]
Sun, Yongqing [3 ]
Tang, Yuan Yan [4 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing, Peoples R China
[2] Chongqing Key Lab Signal & Informat Proc, Chongqing, Peoples R China
[3] NTT Media Intelligence Labs, Yokosuka, Kanagawa, Japan
[4] Univ Macau, Zhuhai UM Sci & Technol Res Inst, Macau, Peoples R China
来源
2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2021年
基金
中国国家自然科学基金;
关键词
D O I
10.1109/SMC52423.2021.9658906
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Deep semi-nonnegative matrix factorization can obtain the hidden hierarchical representations according to the unknown attributes of the given data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intra-class data. Then one hopes to learn a new low dimensional representation which can preserve the intrinsic structure embedded in the original high dimensional data space perfectly. Here we propose a novel distribution preserving deep semi-nonnegative matrix factorization method (DPNMF) to achieve this goal. As a result, the manifold structures in the raw data are well preserved in the feature space being from the top layer. The experimental results on the real-world datasets show that the proposed algorithm has good performance in terms of cluster accuracy and normalized mutual information (NMI).
引用
收藏
页码:1081 / 1086
页数:6
相关论文
共 50 条
  • [11] Two-dimensional semi-nonnegative matrix factorization for clustering
    Peng, Chong
    Zhang, Zhilu
    Chen, Chenglizhao
    Kang, Zhao
    Cheng, Qiang
    INFORMATION SCIENCES, 2022, 590 : 106 - 141
  • [12] Pansharpening with support vector transform and semi-nonnegative matrix factorization
    Hong Li
    Weibin Li
    Shuying Liu
    Multimedia Tools and Applications, 2019, 78 : 7563 - 7578
  • [13] Semi-Nonnegative Matrix Factorization for Motion Segmentation with Missing Data
    Mo, Quanyi
    Draper, Bruce A.
    COMPUTER VISION - ECCV 2012, PT VII, 2012, 7578 : 402 - 415
  • [14] Pansharpening with support vector transform and semi-nonnegative matrix factorization
    Li, Hong
    Li, Weibin
    Liu, Shuying
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (06) : 7563 - 7578
  • [15] DEEP SEMI-NONNEGATIVE MATRIX FACTORIZATION BASED UNSUPERVISED CHANGE DETECTION OF REMOTE SENSING IMAGES
    Yang, Gang
    Li, Heng-Chao
    Yang, Wen
    Emery, William J.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 4917 - 4920
  • [16] Discriminative deep semi-nonnegative matrix factorization network with similarity maximization for unsupervised feature learning
    Wang, Wei
    Chen, Feiyu
    Ge, Yongxin
    Huang, Sheng
    Zhang, Xiaohong
    Yang, Dan
    PATTERN RECOGNITION LETTERS, 2021, 149 : 157 - 163
  • [17] Nonlinear Unmixing of Hyperspectral Data Using Semi-Nonnegative Matrix Factorization
    Yokoya, Naoto
    Chanussot, Jocelyn
    Iwasaki, Akira
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (02): : 1430 - 1437
  • [18] The application of semi-nonnegative matrix factorization for detection of incipient faults in bearings
    Rai, Akhand
    Upadhyay, Sanjay H.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2019, 233 (13) : 4543 - 4555
  • [19] General Semi-Nonnegative Matrix Factorization and Its Application for Statistical Process Monitoring
    Ma, Chuan
    Zhang, Yingwei
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2025, 21 (02) : 1130 - 1139
  • [20] Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data
    Saxena S.
    Kinsella I.
    Musall S.
    Kim S.H.
    Meszaros J.
    Thibodeaux D.N.
    Kim C.
    Cunningham J.
    Hillman E.M.C.
    Churchland A.
    Paninski L.
    PLoS Computational Biology, 2020, 16 (04):