Visual exploration of high-dimensional data through subspace analysis and dynamic projections

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

doi:10.1111/cgf.12639

Title: Visual exploration of high-dimensional data through subspace analysis and dynamic projections

Journal Article · Mon Jun 01 00:00:00 EDT 2015 · Computer Graphics Forum

DOI:https://doi.org/10.1111/cgf.12639· OSTI ID:1326066

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

Univ. of Utah, Salt Lake City, UT (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

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.

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Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002375

OSTI ID:: 1326066

Report Number(s):: DOE-UTAH-PASCUCCI-0014

Journal Information:: Computer Graphics Forum, Vol. 34, Issue 3; ISSN 0167-7055

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 26 works

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Recent research advances on interactive machine learning Jiang, Liu; Liu, Shixia; Chen, Changjian Journal of Visualization, Vol. 22, Issue 2 https://doi.org/10.1007/s12650-018-0531-1	journal	November 2018
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