Γ-stochastic neighbour embedding for feed-forward data visualization
2017 ◽
Vol 17
(4)
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pp. 306-315
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Keyword(s):
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.
2019 ◽
Vol 12
(1)
◽
pp. 16
2017 ◽
Vol 7
(5)
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pp. 665-669
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Keyword(s):
2018 ◽
Vol 6
(8)
◽
pp. 827-830