Manifold Learning for Visualizing and Analyzing High-Dimensional Data

被引:29
|
作者
Zhang, Junping [1 ,2 ]
Huang, Hua [3 ]
Wang, Jue [4 ,5 ]
机构
[1] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China
[2] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Peoples R China
[4] Chinese Acad Sci, Key Lab Complex Syst & Intelligence Sci, Beijing 100864, Peoples R China
[5] Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
10.1109/MIS.2010.8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
引用
收藏
页码:54 / 61
页数:8
相关论文
共 50 条
  • [31] Analyzing high-dimensional cytometry data using FlowSOM
    Katrien Quintelier
    Artuur Couckuyt
    Annelies Emmaneel
    Joachim Aerts
    Yvan Saeys
    Sofie Van Gassen
    Nature Protocols, 2021, 16 : 3775 - 3801
  • [32] THE MANIFOLD SCATTERING TRANSFORM FOR HIGH-DIMENSIONAL POINT CLOUD DATA
    Chew, Joyce
    Steach, Holly
    Viswanath, Siddharth
    Wu, Hau-Tieng
    Hirn, Matthew
    Needell, Deanna
    Vesely, Matthew D.
    Krishnaswamy, Smita
    Perlmutter, Michael
    TOPOLOGICAL, ALGEBRAIC AND GEOMETRIC LEARNING WORKSHOPS 2022, VOL 196, 2022, 196
  • [33] Manifold Discovery for High-Dimensional Data Using Deep Method
    CHEN, J. I. N. G. J. I. N.
    CHEN, S. H. U. P. I. N. G.
    DING, X. U. A. N.
    IEEE ACCESS, 2022, 10 : 65221 - 65227
  • [34] High-dimensional MRI data analysis using a large-scale manifold learning approach
    Loc Tran
    Debrup Banerjee
    Jihong Wang
    Ashok J. Kumar
    Frederic McKenzie
    Yaohang Li
    Jiang Li
    Machine Vision and Applications, 2013, 24 : 995 - 1014
  • [35] High-dimensional MRI data analysis using a large-scale manifold learning approach
    Tran, Loc
    Banerjee, Debrup
    Wang, Jihong
    Kumar, Ashok J.
    McKenzie, Frederic
    Li, Yaohang
    Li, Jiang
    MACHINE VISION AND APPLICATIONS, 2013, 24 (05) : 995 - 1014
  • [36] Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
    Schoeneman, Frank
    Chandola, Varun
    Napp, Nils
    Wodo, Olga
    Zola, Jaroslaw
    2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 1655 - 1660
  • [37] Author Correction: Visualizing structure and transitions in high-dimensional biological data
    Kevin R. Moon
    David van Dijk
    Zheng Wang
    Scott Gigante
    Daniel B. Burkhardt
    William S. Chen
    Kristina Yim
    Antonia van den Elzen
    Matthew J. Hirn
    Ronald R. Coifman
    Natalia B. Ivanova
    Guy Wolf
    Smita Krishnaswamy
    Nature Biotechnology, 2020, 38 : 108 - 108
  • [38] A SOM projection technique with the growing structure for visualizing high-dimensional data
    Wu, Z
    Yen, GG
    PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1763 - 1768
  • [39] A framework for analyzing EEG data using high-dimensional tests
    Zhang, Qiuyan
    Xiang, Wenjing
    Yang, Bo
    Yang, Hu
    BIOINFORMATICS, 2025, 41 (04)
  • [40] Structured sparsity regularization for analyzing high-dimensional omics data
    Vinga, Susana
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (01) : 77 - 87