Conditional t-SNE: more informative t-SNE embeddings

Bo Kang*, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

23 Citations (Scopus)

Abstract

Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyond the specifics of t-SNE, there are two substantial limitations of any such approach: (1) not all information can be captured in a single two-dimensional embedding, and (2) to well-informed users, the salient structure of such an embedding is often already known, preventing that any real new insights can be obtained. Currently, it is not known how to extract the remaining information in a similarly effective manner. We introduce conditional t-SNE (ct-SNE), a generalization of t-SNE that discounts prior information in the form of labels. This enables obtaining more informative and more relevant embeddings. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining an elegant method with a single integrated objective. We show how to efficiently optimize the objective and study the effects of the extra parameter that ct-SNE has over t-SNE. Qualitative and quantitative empirical results on synthetic and real data show ct-SNE is scalable, effective, and achieves its goal: it allows complementary structure to be captured in the embedding and provided new insights into real data.

Original languageEnglish
JournalMachine Learning
DOIs
Publication statusAccepted/In press - 2020

Bibliographical note

Funding Information:
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 615517, from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme, from the FWO (Project Nos. G091017N, G0F9816N, 3G042220), and from the EPSRC (SPHERE EP/R005273/1). We thank Laurens van der Maaten for helpful discussions.

Publisher Copyright:
© 2020, The Author(s).

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Research Groups and Themes

  • Digital Health

Keywords

  • Data visualization
  • Dimensionality reduction
  • Information theory

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