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Title: The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data

Abstract

Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. Here, the proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.

Authors:
 [1];  [2];  [2];  [1];  [3];  [1]
  1. Univ. of Utah, Salt Lake City, UT (United States). Scientific Computing and Imaging Inst.
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Tulane Univ., New Orleans, LA (United States). Dept. of Computer Science
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE; National Science Foundation (NSF)
OSTI Identifier:
1417962
Report Number(s):
LLNL-JRNL-733805
Journal ID: ISSN 0167-7055
Grant/Contract Number:  
AC52-07NA27344; EE0004449; NA0002375; SC0007446; SC0010498
Resource Type:
Accepted Manuscript
Journal Name:
Computer Graphics Forum
Additional Journal Information:
Journal Volume: 35; Journal Issue: 3; Journal ID: ISSN 0167-7055
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; Computer Graphics; Picture/Image Generation; Line and curve generation

Citation Formats

Liu, S., Bremer, P. -T, Jayaraman, J. J., Wang, B., Summa, B., and Pascucci, V. The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data. United States: N. p., 2016. Web. doi:10.1111/cgf.12876.
Liu, S., Bremer, P. -T, Jayaraman, J. J., Wang, B., Summa, B., & Pascucci, V. The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data. United States. doi:10.1111/cgf.12876.
Liu, S., Bremer, P. -T, Jayaraman, J. J., Wang, B., Summa, B., and Pascucci, V. Sat . "The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data". United States. doi:10.1111/cgf.12876. https://www.osti.gov/servlets/purl/1417962.
@article{osti_1417962,
title = {The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data},
author = {Liu, S. and Bremer, P. -T and Jayaraman, J. J. and Wang, B. and Summa, B. and Pascucci, V.},
abstractNote = {Linear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. Here, the proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.},
doi = {10.1111/cgf.12876},
journal = {Computer Graphics Forum},
number = 3,
volume = 35,
place = {United States},
year = {2016},
month = {6}
}

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Cited by: 6 works
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    Works referencing / citing this record:

    Frontier of Information Visualization and Visual Analytics in 2016
    journal, May 2017


    Frontier of Information Visualization and Visual Analytics in 2016
    journal, May 2017