Optimal projections of high dimensional data

被引：3
|
作者：
Corchado, E [1 ]
Fyfe, C [1 ]
机构：
[1] Univ Burgos, Dept Ingn Civil, Burgos, Spain
来源：
2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2002年
关键词：
D O I：
10.1109/ICDM.2002.1184006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we compare two artificial neural network algorithms for performing Exploratory Projection Pursuit, a statistical technique for investigating data by projecting it onto lower dimensional manifolds. The neural networks are extensions of a network which performs Principal Component Analysis. We illustrate the technique on artificial data before applying it to real data.
引用
收藏
页码：589 / 596
页数：8
相关论文
共 50 条
  • [1] Optimal Sets of Projections of High-Dimensional Data
    Lehmann, Dirk J.
    Theisel, Holger
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2016, 22 (01) : 609 - 618
  • [2] Bayesian kernel projections for classification of high dimensional data
    Katarina Domijan
    Simon P. Wilson
    Statistics and Computing, 2011, 21 : 203 - 216
  • [3] Evaluating Clustering in Subspace Projections of High Dimensional Data
    Mueller, Emmanuel
    Guennemann, Stephan
    Assent, Ira
    Seidl, Thomas
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2009, 2 (01):
  • [4] Manual controls for high-dimensional data projections
    Cook, D
    Buja, A
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 1997, 6 (04) : 464 - 480
  • [5] Bayesian kernel projections for classification of high dimensional data
    Domijan, Katarina
    Wilson, Simon P.
    STATISTICS AND COMPUTING, 2011, 21 (02) : 203 - 216
  • [6] Iterative random projections for high-dimensional data clustering
    Cardoso, Angelo
    Wichert, Andreas
    PATTERN RECOGNITION LETTERS, 2012, 33 (13) : 1749 - 1755
  • [7] Measuring the quality of projections of high-dimensional labeled data
    Benato, Barbara C.
    Falcao, Alexandre X.
    Telea, Alexandru C.
    COMPUTERS & GRAPHICS-UK, 2023, 116 : 287 - 297
  • [8] Guided Projections for Analyzing the Structure of High-Dimensional Data
    Ortner, Thomas
    Filzmoser, Peter
    Rohm, Maia
    Breiteneder, Christian
    Brodinova, Sarka
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2018, 27 (04) : 750 - 762
  • [9] Testing homogeneity in high dimensional data through random projections
    Qiu, Tao
    Zhang, Qintong
    Fang, Yuanyuan
    Xu, Wangli
    JOURNAL OF MULTIVARIATE ANALYSIS, 2024, 200
  • [10] ON THE CONDITIONAL DISTRIBUTIONS OF LOW-DIMENSIONAL PROJECTIONS FROM HIGH-DIMENSIONAL DATA
    Leeb, Hannes
    ANNALS OF STATISTICS, 2013, 41 (02): : 464 - 483
12345