runTSNE {flowSpy} | R Documentation |
Calculate t-Distributed Stochastic Neighbor Embedding in FSPY
runTSNE( object, dims = 2, initial_dims = 50, perplexity = 30, theta = 0.5, check_duplicates = TRUE, pca = TRUE, max_iter = 1000, verbose = FALSE, is_distance = FALSE, Y_init = NULL, pca_center = TRUE, pca_scale = FALSE, ... )
object |
an FSPY object |
dims |
integer, Output dimensionality (default: 2) |
initial_dims |
integer. the number of dimensions that should
be retained in the initial PCA step (default: 50). See |
perplexity |
numeric. Perplexity parameter. See |
theta |
numeric. Speed/accuracy trade-off (increase for less accuracy),
set to 0.0 for exact TSNE (default: 0.5). See |
check_duplicates |
logical. Checks whether duplicates are present.
It is best to make sure there are no duplicates present and set this
option to FALSE, especially for large datasets (default: TRUE).
See |
pca, max_iter, is_distance, Y_init, pca_center, pca_scale |
See |
verbose |
logical. Whether to print calculation progress. |
... |
Parameters passing to |
An FSPY object
Maaten, L. Van Der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.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, pp.2579-2605.
if (FALSE) { fspy <- runTSNE(fspy, dims = 2, verbose = TRUE) fspy <- runTSNE(fspy, dims = 2, perplexity = 20, verbose = TRUE) }