Γ-SNE for feed-forward data visualisation

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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.

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  • Γ-stochastic neighbour embedding for feed-forward data visualization

    Accepted author manuscript, 6 MB, PDF-document

    Embargo ends: 7/07/18

Details

Original languageEnglish
JournalInformation Visualization
Volumeaccepted
Early online date7 Jul 2017
DOIs
StateE-pub ahead of print - 7 Jul 2017

Bibliographic 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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