Transcript Experiments
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