论文标题
ENS-T-SNE：同时嵌入t-sne的社区
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
论文作者
论文摘要
当可视化高维数据集时，通常采用了降低降低技术，该技术提供了数据的单个二维视图。我们描述了ENS-T-SNE：一种同时嵌入社区的算法，可以概括T-Stochastic邻里嵌入方法。通过在ENS-T-SNE的3D嵌入中使用不同的观点，可以在同一高维数据集中可视化不同类型的群集。这使观众能够看到并跟踪不同类型的簇，这在提供多个2D嵌入时很难做到，在这些嵌入中，在这些嵌入中，无法轻松识别相应的点。我们说明了ENS-T-SNE使用现实世界应用程序的实用性，并通过不同类型和尺寸的数据集提供了广泛的定量评估。
When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes.
