Γ-stochastic neighbour embedding for feed-forward data visualization

Iain Rice

Research output: Contribution to journalArticle

Abstract

t-distributed Stochastic Neighbour Embedding (t-SNE) is one of the most popular nonlinear dimension reduction techniques used in multiple application domains. In this paper we propose a variation on the embedding neighbourhood distribution, resulting in Γ-SNE, which can construct a feed-forward mapping using an RBF network. We compare the visualizations generated by Γ-SNE with those of t-SNE and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization.
Original languageEnglish
Pages (from-to)306-315
JournalInformation Visualization
Volume17
Issue number4
Early online date7 Jul 2017
DOIs
Publication statusPublished - 1 Oct 2018

Bibliographical note

Γ-SNE for feed-forward data visualisation
Rice, I. 7 Jul 2017 In : Information Visualization. accepted. Copyright © 2017 The Author. Reprinted by permission of SAGE Publications.

Keywords

  • Stochastic neighbour embedding
  • gamma distribution
  • visualization
  • radial basis function network
  • NeuroScale

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