Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

Abstract

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. We show that Isolation kernel addresses two deficiencies of t-SNE that employs Gaussian kernel, and the use of Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods used in speeding up t-SNE.