LAUSR

t-SNE (t-distributed Stochastic Neighbor Embedding) is known to be one of the very powerful tools for dimensionality reduction and data visualization. By adopting the student’s t-distribution in the original SNE (Stochastic Neighbor Embedding), t-SNE achieves faster and more stable learning. However, t-SNE still poses computational complexity due to its dependence on KL-divergence. Our goal is to extend t-SNE in a natural way by the framework of information geometry. Our generalized t-SNE can outperform the original t-SNE with a well-chosen set of parameters. Furthermore, the experimental results for MNIST, Fashion MNIST and COIL-20, show that our generalized t-SNE outperforms the original t-SNE.