Name
Abstract | ||
|---|---|---|
Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10994-011-5273-4 | The International Journal of Advanced Manufacturing Technology |
Keywords | Field | DocType |
Multidimensional scaling,Embedding,Data visualization,Non-metric similarities | Pairwise comparison,Data visualization,Embedding,Pattern recognition,Multidimensional scaling,Visualization,Metric map,Theoretical computer science,Artificial intelligence,Word Association,Metric space,Mathematics | Journal |
Volume | Issue | ISSN |
87 | 1 | 0885-6125 |
Citations | PageRank | References |
28 | 1.18 | 33 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| van der maaten | 1 | 763 | 48.75 |
| geoffrey e hinton | 2 | 40435 | 4751.69 |