ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866909155057467392 |
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| author | Miller, Jacob Huroyan, Vahan Navarrete, Raymundo Hossain, Md Iqbal Kobourov, Stephen |
| author_facet | Miller, Jacob Huroyan, Vahan Navarrete, Raymundo Hossain, Md Iqbal Kobourov, Stephen |
| contents | When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2205_11720 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE Miller, Jacob Huroyan, Vahan Navarrete, Raymundo Hossain, Md Iqbal Kobourov, Stephen Machine Learning Data Structures and Algorithms Human-Computer Interaction When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes. |
| title | ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE |
| topic | Machine Learning Data Structures and Algorithms Human-Computer Interaction |
| url | https://arxiv.org/abs/2205.11720 |