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. Employing a blind method for drift management adjusts the embedding space, facilitating continuous visualisation of evolving data dynamics. Our experimental evaluations demonstrate the effectiveness and efficiency of S+t-SNE. The results highlight 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.

中文翻译：

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S+t-SNE - 为数据流带来降维

我们提出了 S+t-SNE，它是 t-SNE 算法的改进版，旨在处理无限数据流。 S+t-SNE 背后的核心思想是随着新数据的到来逐步更新 t-SNE 嵌入，确保处理流场景的可扩展性和适应性。通过在每一步选择最重要的点，该算法确保了可扩展性，同时保持信息丰富的可视化。采用盲法进行漂移管理可以调整嵌入空间，促进不断变化的数据动态的连续可视化。我们的实验评估证明了 S+t-SNE 的有效性和效率。结果凸显了它在流场景中捕获模式的能力。我们希望我们的方法为研究人员和从业者提供理解和解释高维数据的实时工具。