Abstract
High dimensional data is difficult to visualize. T-Distributed Stochastic Neighbor Embedding (t-SNE) is a popular technique for dimensionality reduction enabling a planar visualization of a dataset preserving as much as possible its metric. This paper explores the theoretical background of t-SNE and its accelerated version. A comparison of the performance of t-SNE on various datasets with different dimensions is also performed.
| Original language | English |
|---|---|
| Pages (from-to) | 250-270 |
| Number of pages | 21 |
| Journal | Image Processing On Line |
| Volume | 14 |
| Early online date | 31 Oct 2024 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2024 IPOL & the authors.
Keywords
- Barnes-Hut
- dimensionality reduction
- manifold learning
- SNE
- t-SNE
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