On the Attractive and Repulsive Forces of Generalized Stochastic Neighbor Embedding With Alpha-Divergence
Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be crowded due to the dimensionality reduction. To mitigate the crowding problem, cert...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IEEE
2024-01-01
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| シリーズ: | IEEE Access |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/10577172/ |
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