实时追踪嵌入空间中的主题演变，动态呈现数据随时间的语义变化。
Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces
- 融合力导向投影与流式聚类，构建时空映射可视化方法。
- 支持文本与图像数据的实时主题演化探索，交互响应迅速。
- 适合研究艺术创作、社交媒体等时间序列数据的学者与分析师。
许多现实世界数据——从艺术家的作品集到个人社交媒体历史——都表现出随时间有意义的语义变化，而现有降维方法难以捕捉此类动态。为弥补这一差距，我们提出一种结合力导向投影与流式聚类的可视化技术，构建嵌入空间的时空地图。基于此技术，我们开发了Chronotome，一个用于实时交互探索时间序列数据中主题演变的工具。通过文本和图像数据的应用案例，我们展示了该方法如何为理解时序数据的美学与语义提供新视角。
原文摘要 · Abstract (English)
Many real-world datasets -- from an artist's body of work to a person's social media history -- exhibit meaningful semantic changes over time that are difficult to capture with existing dimensionality reduction methods. To address this gap, we introduce a visualization technique that combines force-based projection and streaming clustering methods to build a spatial-temporal map of embeddings. Applying this technique, we create Chronotome, a tool for interactively exploring evolving themes in time-based data -- in real time. We demonstrate the utility of our approach through use cases on text and image data, showing how it offers a new lens for understanding the aesthetics and semantics of temporal datasets.
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