Supervised Stochastic Neighbor Embedding Using Contrastive Learning
Yi Zhang
Stochastic neighbor embedding (SNE) methods $t$-SNE, UMAP are two most
popular dimensionality reduction methods for data visualization. Contrastive
learning, especially self-supervised contrastive learning (SSCL), has showed
great success in embedding features from unlabeled data. The conceptual
connection between SNE and SSCL has been exploited. In this work, within the
scope of preserving neighboring information of a dataset, we extend the
self-supervised contrastive approach to the fully-supervised setting, allowing
us to effectively leverage label information. Clusters of samples belonging to
the same class are pulled together in low-dimensional embedding space, while
simultaneously pushing apart clusters of samples from different classes.