skip to main content


Title: 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. 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.
 [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:
Report Number(s):
Journal ID: ISSN 0167-7055
Grant/Contract Number:
AC52-07NA27344; EE0004449; NA0002375; SC0007446; SC0010498
Accepted Manuscript
Journal Name:
Computer Graphics Forum
Additional Journal Information:
Journal Volume: 35; Journal Issue: 3; Journal ID: ISSN 0167-7055
Research Org:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org:
USDOE; National Science Foundation (NSF)
Country of Publication:
United States
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; Computer Graphics; Picture/Image Generation; Line and curve generation
OSTI Identifier: