修正的条件 t-SNE：超越最近邻的视角
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
AI总结：
本文针对条件 t-SNE 在标签聚类良好时失效的问题，提出在高维相似度上施加条件并分别存储标签内外最近邻的修正方法，在提升嵌入质量的同时改善了可扩展性。
摘要：
条件 t-SNE（ct-SNE）是 t-SNE 的一个近期扩展，它允许从嵌入中移除已知的簇信息，从而获得揭示标签信息之外结构的可视化结果。例如，当人们希望剔除一组类别之间不想要的差异时，这一方法十分有用。我们表明，ct-SNE 在许多现实场景中会失效，即当数据在原始高维空间中按照标签已经形成良好聚类时。我们提出一种修正方法：对高维相似度而非低维相似度施加条件，并分别存储标签内和标签间的最近邻。这也使得最近提出的 t-SNE 加速方法得以应用，从而提高了可扩展性。通过在合成数据上的实验，我们发现所提出的方法解决了上述问题并提升了嵌入质量。在包含批次效应的真实数据上，预期的改进并不总是出现。我们认为，鉴于其改进的可扩展性，修正的 ct-SNE 总体上更可取。实验结果还凸显了新的开放问题，例如如何处理簇之间的距离变化。
英文摘要：
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond label information. This is useful, for example, when one wants to factor out unwanted differences between a set of classes. We show that ct-SNE fails in many realistic settings, namely if the data is well clustered over the labels in the original high-dimensional space. We introduce a revised method by conditioning the high-dimensional similarities instead of the low-dimensional similarities and storing within- and across-label nearest neighbors separately. This also enables the use of recently proposed speedups for t-SNE, improving the scalability. From experiments on synthetic data, we find that our proposed method resolves the considered problems and improves the embedding quality. On real data containing batch effects, the expected improvement is not always there. We argue revised ct-SNE is preferable overall, given its improved scalability. The results also highlight new open questions, such as how to handle distance variations between clusters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
