Data Visualization With Multidimensional Scaling

By Andreas Buja, Deborah F. Swayne, Michael L. Littman, N. Dean, Heike Hofmann, Lisha Chen. 2008

336 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/939c9c91aa10c02367ebe2a44f5398fe22e4e559

How researchers see this paper

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

In Artificial Intelligence

1. Mehrdad Honarkhah
2. Xiaofei Xu
3. Shuang Wang
4. T. Chow
5. Q. M. Jonathan Wu
6. Haijun Zhang
7. Jef Caers
8. Philippe Renard

In Computer Vision and Pattern Recognition

1. Paul Vickers
2. Xiaofei Xu
3. Helen Gibson
4. Shuang Wang
5. T. Chow
6. Q. M. Jonathan Wu
7. Joe Faith
8. Haijun Zhang

In Statistical and Nonlinear Physics

1. Paul Vickers
2. Helen Gibson
3. Joe Faith
4. Jef Caers
5. Philippe Renard
6. A. Alcolea
7. Gregoire Mariethoz
8. Louis J. Durlofsky

Citations in the corpus

• Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling (2010, methodology): “The starting idea for improving MDS speed is the anchor point MDS method ( Buja et al. 2008 ).”
• Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data (2018, background): “Instead of SOM, it is possible to design multilayer versions of multidimensional scaling [51], multivariate analysis [52], and t-distributed stochastic neighbor embedding [53] for addressing tree-structured data.”
• A survey of two-dimensional graph layout techniques for information visualisation (2013, methodology): “In addition, Buja et al.46 have implemented it as part of the XGvis system.”