Γ-stochastic neighbour embedding for feed-forward data visualization

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.

Publication DOI: https://doi.org/10.1177/1473871617715212
Divisions: College of Engineering & Physical Sciences
Additional Information: 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.
Uncontrolled Keywords: Stochastic neighbour embedding, gamma distribution,visualization, radial basis function network,NeuroScale
Publication ISSN: 1473-8724
Last Modified: 08 Jan 2024 08:29
Date Deposited: 06 Sep 2017 10:30
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Related URLs: https://journal ... 473871617715212 (Publisher URL)
PURE Output Type: Article
Published Date: 2018-10-01
Published Online Date: 2017-07-07
Accepted Date: 2017-07-06
Authors: Rice, Iain (ORCID Profile 0000-0003-4814-8920)

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