Γ-stochastic neighbour embedding for feed-forward data visualization. (October 2018)

• Record Type: Journal Article
• Authors: Rice, Iain
• Is Part Of: Information visualization. Volume 17:Number 4(2018)
• Page Start: 306
• Page End: 315
• Publication Date: 2018-10
• Subjects: Stochastic neighbour embedding -- gamma distribution -- visualization -- radial basis function network -- NeuroScale; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/1473871617715212
• Languages: English
• ISSNs: 1473-8716

Abstract

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.