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Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections
Author(s) -
Liu S.,
Wang B.,
Thiagarajan J. J.,
Bremer P.T.,
Pascucci V.
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12639
Subject(s) - subspace topology , computer science , linear subspace , cluster analysis , dimension (graph theory) , graph , spectral clustering , clustering high dimensional data , novelty , viewpoints , visualization , artificial intelligence , pattern recognition (psychology) , data mining , theoretical computer science , mathematics , art , philosophy , geometry , theology , pure mathematics , visual arts
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.