Visualizing High-Dimensional Data: Advances in the Past Decade

By Shusen Liu, D. Maljovec, Bei Wang, Peer-Timo Bremer, Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics, 2017

447 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Visualizing the Hidden Activity of Artificial Neural Networks (2017, background): “Compared to other high-dimensional data visualizations, such as scatterplot matrices, parallel coordinates, star coordinates, and their variants, projections are considerably more scalable with respect to the number of dimensions [25, 44].”
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization (2020, background): “Many visual analytics tools have been developed to help deep learning experts analyze their models and predictions [5,15,23,27,36,37].”
Toward a Quantitative Survey of Dimension Reduction Techniques (2019, background): “As such, high-dimensional visualization has become an important sub-ﬁeld of Information Visualization (InfoVis) [1], [2], [3], [4].”
The future of risk assessment (2018, background): “It also renders difficult the a posteriori scenario analysis for risk understanding, due to the difficulty of visualization of the results in such large spaces; this requires the development of specialized representation tools [56,113,118,121].”