Class-aware t-SNE: cat-SNE

De Bodt, Cyril [UCL] Mulders, Dounia [UCL] Lopez Sanchez, Daniel [UCL] Verleysen, Michel [UCL] Lee, John Aldo [UCL]

Stochastic Neighbor Embedding (SNE) and variants like t-distributed SNE are popular methods of unsupervised dimensionality reduction (DR) that deliver outstanding experimental results. Regular t-SNE is often used to visualize data with class labels in colored scatterplots, even if those labels are actually not involved in the DR process. This paper proposes a modification of t-SNE that employs class labels to adjust the widths of the Gaussian neighborhoods around each datum, instead of deriving those from a perplexity set by the user. The widths are fixed to concentrate a major fraction of the probability distribution around a datum on neighbors with the same class. This tends to shrink the bulk of the classes and to stretch their low-dimensional separation. Experimental results show that the proposed class-aware t-SNE (cat-SNE) outperforms regular t-SNE in KNN classification tasks carried out in the embedding.