Targeted projection pursuit for interactive exploration of high-dimensional data sets

被引:0
|
作者
Faith, Joe [1 ]
机构
[1] Northumbria Univ, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
High-dimensional data is, by its nature, difficult to visualise. Many current techniques involve reducing the dimensionality of the data, which results in a loss of information. Targeted Projection Pursuit is a novel method for visualising high-dimensional datasets which allows the user to interactively explore the space of possible views to find those that meet their requirements. A prototype tool that utilises this method is introduced, and is shown to allow users to explore data through an interface that is transparent and efficient. The tool and underlying technique are general purpose - applicable to any high-dimensional numeric data, and supporting a wide range of exploratory data analysis activities - but are evaluated on three particular tasks using gene expression data: identifiying discriminatory genes, visualising diagnostic classes, and detecting misdiagnosed samples. It is found to perform well in comparison with standard techniques.
引用
收藏
页码:286 / 292
页数:7
相关论文
共 50 条
  • [1] DACC: A Data Exploration Method for High-Dimensional Data Sets
    Zhao, Qingnan
    Li, Hui
    Chen, Mei
    Dai, Zhenyu
    Zhu, Ming
    [J]. ARTIFICIAL INTELLIGENCE AND ALGORITHMS IN INTELLIGENT SYSTEMS, 2019, 764 : 219 - 229
  • [2] Very Fast Interactive Visualization of Large Sets of High-dimensional Data
    Dzwinel, Witold
    Wcislo, Rafal
    [J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE, 2015, 51 : 572 - 581
  • [3] Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data
    Urpa, Lea M.
    Anders, Simon
    [J]. BMC BIOINFORMATICS, 2019, 20 (1)
  • [4] Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data
    Lea M. Urpa
    Simon Anders
    [J]. BMC Bioinformatics, 20
  • [5] Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data
    Zhou, Youjia
    Chalapathi, Nithin
    Rathore, Archit
    Zhao, Yaodong
    Wang, Bei
    [J]. 2021 IEEE 14TH PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS 2021), 2021, : 101 - 110
  • [6] Interactive Exploration of High-Dimensional Phase Diagrams
    van de Walle, Axel
    Chen, Hantong
    Liu, Helena
    Nataraj, Chiraag
    Samanta, Sayan
    Zhu, Siya
    Arroyave, Raymundo
    [J]. JOM, 2022, 74 (09) : 3478 - 3486
  • [7] Interactive Exploration of High-Dimensional Phase Diagrams
    Axel van de Walle
    Hantong Chen
    Helena Liu
    Chiraag Nataraj
    Sayan Samanta
    Siya Zhu
    Raymundo Arroyave
    [J]. JOM, 2022, 74 : 3478 - 3486
  • [8] iStar (i*): An interactive star coordinates approach for high-dimensional data exploration
    Zanabria, Germain Garcia
    Nonato, Luis Gustavo
    Gomez-Nieto, Erick
    [J]. COMPUTERS & GRAPHICS-UK, 2016, 60 : 107 - 118
  • [9] Exploration of High-Dimensional Nuclei Data
    Fuentes, Fernando
    Kettani, Houssain
    Ostrouchov, George
    Stoitsov, Mario
    Nam, Hai Ah
    [J]. 2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 4, 2010, : 1 - 4
  • [10] Honest Confidence Sets for High-Dimensional Regression by Projection and Shrinkage
    Zhou, Kun
    Li, Ker-Chau
    Zhou, Qing
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2023, 118 (541) : 469 - 488