Visualizing non-metric similarities in multiple maps
Presenter: YU-TING LU
Authors: Laurens van der Maaten and Geoffrey Hinton
2012. ML
Intelligent Database Systems Lab
Outlines
 Motivation
 Objectives
 Methodology
 Experiments
 Conclusions
 Comments
Intelligent Database Systems Lab
Motivation
• Techniques for multidimensional
scaling(MDS) are subject to the fundamental
limitations of metric spaces in a visualization.
• Multidimensional scaling cannot faithfully
represent intransitive pairwise similarities in
a visualization, and it cannot faithfully
visualize “central” objects.
Intelligent Database Systems Lab
Objectives
• This study present an extension of multidimensional
scaling technique multiple maps t-SNE.
• The aims to address the problems of traditional
multidimensional scaling techniques when visualize
non-metric similarities.
• By constructing a collection of maps that reveal
complementary structure in the similarity data.
Intelligent Database Systems Lab
Methodology(review: t-SNE)
Intelligent Database Systems Lab
Methodology-Multiple maps t-SNE
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the word association data set(a-e)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the word association data set(a-e)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the word association data set(a-e)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the word association data set(a-e)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the word association data set(a-e)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the word association data set(a-e)
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the NIPS co-authorship data set(a-d)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the NIPS co-authorship data set(a-d)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the NIPS co-authorship data set(a-d)
Intelligent Database Systems Lab
Experiments
Results of multiple maps t-SNE on the NIPS co-authorship data set(a-d)
Intelligent Database Systems Lab
Experiments
Intelligent Database Systems Lab
Conclusions
• This paper is to construct visualizations that are not
hampered by the two main limitations of metric spaces.
• Apply multiple maps t-SNE to a large data set of word
association data and to a data set of NIPS co-
authorships, demonstrating its ability to successfully
visualize non-metric similarities.
Intelligent Database Systems Lab
Comments
• Advantages
- Faithfully visualizing non-metric similarity data
• Applications
- Data visualization.
- Non-metric similarities.
Intelligent Database Systems Lab