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

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. How...

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Bibliographic Details
Published in:Computer graphics forum Vol. 35; no. 3; pp. 1 - 10
Main Authors: Liu, S., Bremer, P.-T, Jayaraman, J. J., Wang, B., Summa, B., Pascucci, V.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.06.2016
Wiley
Subjects:
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Computer Graphics
Exploration
I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation
Line and curve generation
MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Picture/Image Generation
Pictures
Projection
Ranking
Studies
Visualization
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Description
Summary: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. 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.
Bibliography:istex:BE613F9CEA7307F484522B99C9C60CE3957EF235
ArticleID:CGF12876
Supporting Information
ark:/67375/WNG-0XPCB43F-L
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
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USDOE
AC52-07NA27344; EE0004449; NA0002375; SC0007446; SC0010498
National Science Foundation (NSF)
LLNL-JRNL-733805
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12876