tsne.centralities: t-Distributed Stochastic Neighbor Embedding (t-SNE) on...

In jafarilab/CINNA: Deciphering Central Informative Nodes in Network Analysis

t-Distributed Stochastic Neighbor Embedding (t-SNE) on centrality measures

Description

This function applies t-SNE, dimensionality reduction algorithm, on centrality measures.

Usage

tsne.centralities(x, dims = 2, perplexity = 5, scale = TRUE)

Arguments

x	a list containg the computed cetrality values
dims	integer; number of the outpu dimensions(default=2)
perplexity	numeric; A flexible measure of the efficient number of neighbors. The performance of SNE is fairly robust to changes in the perplexity, and typical values are between 5 and 50.(default=5)
scale	Whether the centrality values should be scaled or not(default=TRUE)

Details

t-SNE is a non-linear dimensionality reduction algorithm used for exploring high-dimensional data. Here, It maps multi-dimensional centrality measure data to less dimensions suitable to work with it.

Value

It resturns cost plot of tsne results which displays centralities in order of their corresponding costs.

Author(s)

Minoo Ashtiani, Mohieddin Jafari

References

van der Maaten, L. (2014). Accelerating t SNE using Tree Based Algorithms. Journal of Machine Learning Research, 15, 3221–3245. Van Der Maaten, L. J. P., & Hinton, G. E. (2008). Visualizing high dimensional data using t sne. Journal of Machine Learning Research, 9, 2579–2605.

See Also

Rtsne

jafarilab/CINNA documentation built on Aug. 19, 2023, 4:49 p.m.