Or Patashnik, Min Lu, Amit H Bermano, Daniel Cohen-Or
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引用次数: 1
Abstract
Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.
期刊介绍:
Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media.
Computational Visual Media publishes articles that focus on, but are not limited to, the following areas:
• Editing and composition of visual media
• Geometric computing for images and video
• Geometry modeling and processing
• Machine learning for visual media
• Physically based animation
• Realistic rendering
• Recognition and understanding of visual media
• Visual computing for robotics
• Visualization and visual analytics
Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope.
This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.