Visual exploration of high-dimensional data through subspace analysis and dynamic projections
Abstract
Here, we introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.
- Authors:
-
- Univ. of Utah, Salt Lake City, UT (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Univ. of Utah, Salt Lake City, UT (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1326066
- Report Number(s):
- DOE-UTAH-PASCUCCI-0014
Journal ID: ISSN 0167-7055
- Grant/Contract Number:
- NA0002375
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computer Graphics Forum
- Additional Journal Information:
- Journal Volume: 34; Journal Issue: 3; Journal ID: ISSN 0167-7055
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; high-dimensional data; subspace clustering
Citation Formats
Liu, S., Wang, B., Thiagarajan, J. J., Bremer, P. -T., and Pascucci, V. Visual exploration of high-dimensional data through subspace analysis and dynamic projections. United States: N. p., 2015.
Web. doi:10.1111/cgf.12639.
Liu, S., Wang, B., Thiagarajan, J. J., Bremer, P. -T., & Pascucci, V. Visual exploration of high-dimensional data through subspace analysis and dynamic projections. United States. https://doi.org/10.1111/cgf.12639
Liu, S., Wang, B., Thiagarajan, J. J., Bremer, P. -T., and Pascucci, V. Mon .
"Visual exploration of high-dimensional data through subspace analysis and dynamic projections". United States. https://doi.org/10.1111/cgf.12639. https://www.osti.gov/servlets/purl/1326066.
@article{osti_1326066,
title = {Visual exploration of high-dimensional data through subspace analysis and dynamic projections},
author = {Liu, S. and Wang, B. and Thiagarajan, J. J. and Bremer, P. -T. and Pascucci, V.},
abstractNote = {Here, we introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.},
doi = {10.1111/cgf.12639},
journal = {Computer Graphics Forum},
number = 3,
volume = 34,
place = {United States},
year = {Mon Jun 01 00:00:00 EDT 2015},
month = {Mon Jun 01 00:00:00 EDT 2015}
}
Web of Science
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