Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors

TL;DR

This paper introduces a revised version of conditional t-SNE that conditions on high-dimensional similarities rather than low-dimensional ones, improving scalability and embedding quality in many scenarios, especially when data is well clustered.

Contribution

The authors propose a new method for conditional t-SNE that conditions on high-dimensional similarities and separates within- and across-label neighbors, enhancing scalability and performance.

Findings

Revised ct-SNE improves embedding quality on synthetic data.

The method offers better scalability with recent t-SNE speedups.

Performance on real data with batch effects varies, indicating open challenges.

Abstract

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.