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Title: 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:
 [1];  [1];  [2];  [2];  [1]
+ Show Author Affiliations
  1. Univ. of Utah, Salt Lake City, UT (United States)
  2. 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.
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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
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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.
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@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}
}
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https://doi.org/10.1111/cgf.12639
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Cited by: 26 works
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Works referenced in this record:

The Grand Tour: A Tool for Viewing Multidimensional Data
journal, January 1985

  • Asimov, Daniel
  • SIAM Journal on Scientific and Statistical Computing, Vol. 6, Issue 1
  • DOI: 10.1137/0906011

Computational Methods for High-Dimensional Rotations in Data Visualization
book, January 2005

Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
journal, July 1997

  • Belhumeur, P. N.; Hespanha, J. P.; Kriegman, D. J.
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, Issue 7
  • DOI: 10.1109/34.598228

Entropy-based subspace clustering for mining numerical data
conference, January 1999

  • Cheng, Chun-Hung; Fu, Ada Waichee; Zhang, Yi
  • Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '99
  • DOI: 10.1145/312129.312199

Sparse subspace clustering
conference, June 2009

  • Elhamifar, Ehsan; Vidal, Rene
  • 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2009 IEEE Conference on Computer Vision and Pattern Recognition
  • DOI: 10.1109/CVPR.2009.5206547

A New Statistical Approach to Geographic Variation Analysis
journal, September 1969

  • Gabriel, K. Ruben; Sokal, Robert R.
  • Systematic Zoology, Vol. 18, Issue 3
  • DOI: 10.2307/2412323

Algebraic Geometry
book, January 1992

Graphs as navigational infrastructure for high dimensional data spaces
journal, February 2011

Direct numerical simulation of ignition front propagation in a constant volume with temperature inhomogeneities
journal, April 2006

Introduction to manifold learning: Introduction to manifold learning
journal, July 2012

  • Izenman, Alan Julian
  • Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 4, Issue 5
  • DOI: 10.1002/wics.1222

Robust Recovery of Subspace Structures by Low-Rank Representation
journal, January 2013

  • Liu, Guangcan; Lin, Zhouchen; Yan, Shuicheng
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 1
  • DOI: 10.1109/TPAMI.2012.88

Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data: Interactive Data Exploration
journal, June 2014

  • Liu, S.; Wang, B.; Bremer, P. -T.
  • Computer Graphics Forum, Vol. 33, Issue 3
  • DOI: 10.1111/cgf.12366

Visualizing the quality of dimensionality reduction
journal, July 2013

A Framework for Exploring Multidimensional Data with 3D Projections
journal, June 2011

Subspace clustering for high dimensional data: a review
journal, June 2004

  • Parsons, Lance; Haque, Ehtesham; Liu, Huan
  • ACM SIGKDD Explorations Newsletter, Vol. 6, Issue 1
  • DOI: 10.1145/1007730.1007731

GGobi: evolving from XGobi into an extensible framework for interactive data visualization
journal, August 2003

  • Swayne, Deborah F.; Lang, Duncan Temple; Buja, Andreas
  • Computational Statistics & Data Analysis, Vol. 43, Issue 4
  • DOI: 10.1016/S0167-9473(02)00286-4

Subspace search and visualization to make sense of alternative clusterings in high-dimensional data
conference, October 2012

  • Tatu, Andrada; Maas, Fabian; Farber, Ines
  • 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)
  • DOI: 10.1109/VAST.2012.6400488

A Global Geometric Framework for Nonlinear Dimensionality Reduction
journal, December 2000

ClustNails: Visual analysis of subspace clusters
journal, August 2012

  • Tatu, Andrada; Zhang, Leishi; Bertini, Enrico
  • Tsinghua Science and Technology, Vol. 17, Issue 4
  • DOI: 10.1109/TST.2012.6297588

Subspace Clustering
journal, March 2011

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