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Title: Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections

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:
 [1] ;  [1] ;  [2] ;  [2] ;  [1]
  1. Scientific Computing and Imaging Institute, University of Utah
  2. Lawrence Livermore National Laboratory
Publication Date:
OSTI Identifier:
1221563
Report Number(s):
DOE-UTAH-PASCUCCI-0003
Journal ID: ISSN 0167-7055
DOE Contract Number:
NA0002375
Resource Type:
Journal Article
Resource Relation:
Journal Name: Computer Graphics Forum; Journal Volume: 34; Journal Issue: 3
Research Org:
University of Utah
Sponsoring Org:
USDOE; USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING HPC, Data Management