Novel Loss Functions for Improved Data Visualization in t-SNE

Today's article comes from the journal of Machine Learning and Knowledge Extraction. The authors are Nassar et al., from Hamad Bin Khalifa University, in Qatar. In this paper they're evaluating two replacements for KL-divergence within t-SNE. Max-Flipped KL Divergence (KLmax) and KL-Wasserstein Loss.