mp (version 0.3.1)

tSNE: t-Distributed Stochastic Neighbor Embedding

Description

Creates a k-dimensional representation of the data by modeling the probability of picking neighbors using a Gaussian for the high-dimensional data and t-Student for the low-dimensional map and then minimizing the KL divergence between them. This implementation uses the same default parameters as defined by the authors.

Usage

tSNE(X, Y = NULL, k = 2, perplexity = 30, n.iter = 1000)

Arguments

X

A data frame, data matrix, dissimilarity (distance) matrix or dist object.

Y

Initial k-dimensional configuration. If NULL, the method uses a random initial configuration.

k

Target dimensionality. Avoid anything other than 2 or 3.

perplexity

A rough upper bound on the neighborhood size.

n.iter

Number of iterations to perform.

Value

The k-dimensional representation of the data.

References

L.J.P. van der Maaten and G.E. Hinton. _Visualizing High-Dimensional Data Using t-SNE._ Journal of Machine Learning Research 9(Nov): 2579-2605, 2008.

Examples

# Iris example
emb <- tSNE(iris[, 1:4])
plot(emb, col=iris$Species)