Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.

ABSTRACT: T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the 'crowding problem' of SNE. Here, we develop an efficient implementation of t-SNE for a t-distribution kernel with an arbitrary degree of freedom ν, with ν → ∞ corresponding to SNE and ν = 1 corresponding to the standard t-SNE. Using theoretical analysis and toy examples, we show that ν < 1 can further reduce the crowding problem and reveal finer cluster structure that is invisible in standard t-SNE. We further demonstrate the striking effect of heavier-tailed kernels on large real-life data sets such as MNIST

SUBMITTER: Kobak D 

PROVIDER: S-EPMC7582035 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature