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.

我们介绍了一种名为 S+t-SNE 的 t-SNE 算法的改进版本，它专门用于处理无限数据流，通过在新数据到达时增量更新 t-SNE 嵌入，保证了可扩展性和适应流式场景的能力，使用盲目方法进行漂移管理，调整嵌入空间，实现对不断演变的数据动态的持续可视化，实验评估结果表明 S+t-SNE 的效果和效率，突出其在流式场景中捕捉模式的能力，我们希望该方法能为研究人员和实践者提供一种实时理解和解释高维数据的工具。