%0 Journal Article
%T ¦£
%A Iain Rice
%J Information Visualization
%@ 1473-8724
%D 2018
%R 10.1177/1473871617715212
%X 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 ¦£-stochastic neighbour embedding, which can construct a feed-forward mapping using a radial basis function network. We compare the visualizations generated by ¦£-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
%K Stochastic neighbour embedding
%K gamma distribution
%K visualization
%K radial basis function network
%K NeuroScale
%U https://journals.sagepub.com/doi/full/10.1177/1473871617715212