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

Liu, S.; Bremer, P. -T; Jayaraman, J. J.; Wang, B.; Summa, B.; Pascucci, V.

doi:10.1111/cgf.12876

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

Journal Article · Sat Jun 04 00:00:00 EDT 2016 · Computer Graphics Forum

DOI:https://doi.org/10.1111/cgf.12876· OSTI ID:1417962

Liu, S. ^[1]; Bremer, P. -T ^[2]; Jayaraman, J. J. ^[2]; Wang, B. ^[1]; Summa, B. ^[3]; Pascucci, V. ^[1]

Univ. of Utah, Salt Lake City, UT (United States). Scientific Computing and Imaging Inst.
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Tulane Univ., New Orleans, LA (United States). Dept. of Computer Science

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.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF)

Grant/Contract Number:: AC52-07NA27344; EE0004449; NA0002375; SC0007446; SC0010498

OSTI ID:: 1417962

Report Number(s):: LLNL-JRNL-733805

Journal Information:: Computer Graphics Forum, Vol. 35, Issue 3; ISSN 0167-7055

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 14 works

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Frontier of Information Visualization and Visual Analytics in 2016 Lu, Min; Chen, Siming; Lai, Chufan Journal of Visualization, Vol. 20, Issue 4 https://doi.org/10.1007/s12650-017-0431-9	journal	May 2017
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Recent Research Advances on Interactive Machine Learning Jiang, Liu; Liu, Shixia; Chen, Changjian arXiv https://doi.org/10.48550/arxiv.1811.04548	preprint	January 2018

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