S+t-SNE -- Bringing Dimensionality Reduction to Data Streams
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916573116104704 |
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| author | Vieira, Pedro C. Montrezol, João P. Vieira, João T. Gama, João |
| author_facet | Vieira, Pedro C. Montrezol, João P. Vieira, João T. Gama, João |
| contents | We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle streaming scenarios. By selecting the most important points at each step, the algorithm ensures scalability while keeping informative visualisations. By employing a blind method for drift management, the algorithm adjusts the embedding space, which facilitates the visualisation of evolving data dynamics. Our experimental evaluations demonstrate the effectiveness and efficiency of S+t-SNE, whilst highlighting its ability to capture patterns in a streaming scenario. We hope our approach offers researchers and practitioners a real-time tool for understanding and interpreting high-dimensional data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | S+t-SNE -- Bringing Dimensionality Reduction to Data Streams Vieira, Pedro C. Montrezol, João P. Vieira, João T. Gama, João Artificial Intelligence Information Retrieval We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle streaming scenarios. By selecting the most important points at each step, the algorithm ensures scalability while keeping informative visualisations. By employing a blind method for drift management, the algorithm adjusts the embedding space, which facilitates the visualisation of evolving data dynamics. Our experimental evaluations demonstrate the effectiveness and efficiency of S+t-SNE, whilst highlighting its ability to capture patterns in a streaming scenario. We hope our approach offers researchers and practitioners a real-time tool for understanding and interpreting high-dimensional data. |
| title | S+t-SNE -- Bringing Dimensionality Reduction to Data Streams |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2403.17643 |