Γ-stochastic neighbour embedding for feed-forward data visualization. (October 2018) - Rice, Iain
T-Distributed stochastic neighbour embedding is one of the most popular non-linear dimension-reduction techniques used in multiple application domains. In this article, we propose a variation on the embedding neighbourhood distribution, resulting in Γ-stochastic neighbour embedding, which can construct a feed-forward mapping using a radial basis function network. We compare the visualizations generated by Γ-stochastic neighbour embedding with those of t-distributed stochastic neighbour embedding and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization.