| tsne_centralities {CINNA} | R Documentation |
t-Distributed Stochastic Neighbor Embedding (t-SNE) on centrality measures
Description
This function applies t-SNE, a dimensionality reduction algorithm, to centrality measures.
Usage
tsne_centralities(x, dims = 2, perplexity = 5, scale = TRUE)
Arguments
x |
A list containing the computed centrality values. |
dims |
An integer specifying the number of output dimensions (default = 2). |
perplexity |
A numeric value representing a flexible measure of the efficient number of neighbors. The performance of t-SNE is fairly robust to changes in perplexity, and typical values are between 5 and 50 (default = 5). |
scale |
A logical value indicating 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. It maps multi-dimensional centrality measure data to a lower-dimensional space suitable for analysis and visualization.
Value
A cost plot of t-SNE results, which displays centralities in order of their corresponding costs. The cost plot provides information about the optimization process and the quality of the embedding.
A cost plot of t-SNE results, which displays centralities in order of their corresponding costs. The cost plot is a ggplot object that represents the optimization process and the quality of the embedding. The x-axis represents the iterations of the t-SNE algorithm, and the y-axis represents the cost associated with each iteration. The cost measures the discrepancy between the original high-dimensional space and the low-dimensional embedding. By examining the cost plot, you can assess the convergence and stability of the t-SNE algorithm and evaluate the quality of the embedding.
Author(s)
Minoo Ashtiani, Mehdi Mirzaie, 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.