The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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. 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.
Description
@article{10.1111:cgf.12876,
journal = {Computer Graphics Forum},
title = {{The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data}},
author = {Liu, Shusen and Bremer, Peer-Timo and Jayaraman, Jayaraman Thiagarajan and Wang, Bei and Summa, Brian and Pascucci, Valerio},
year = {2016},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.12876}
}