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tSNE

Source: R/tSNE_class.R
tSNE.Rd

t-Distributed Stochastic Neighbor Embedding.

Usage

tSNE(
  dims = 2,
  perplexity = 30,
  max_iter = 100,
  theta = 0.5,
  check_duplicates = FALSE,
  init = NULL,
  eta = 200,
  ...
)

Arguments

dims

(numeric) The number of tSNE dimensions computed. The default is 2.

perplexity

(numeric) Perplexity parameter. The default is 30.

max_iter

(numeric) The maximum number of tSNE iterations. The default is 100.

theta

(numeric) Speed/accuracy trade-off. A value of 0 gives an exact tSNE. The default is 0.5.

check_duplicates

(logical) Check for duplicates. Allowed values are limited to the following:

  • "TRUE": Checks for the presence of exact duplicate samples.

  • "FALSE": Does not check for exact duplicate samples.

The default is FALSE.

init

(NULL, data.frame, DatasetExperiment) A set of coordinates for initialising the tSNE algorithm. NULL uses random initialisation. The default is NULL.

eta

(numeric) The learning rate parameter. The default is 200.

...

Additional slots and values passed to struct_class.

Value

A tSNE object with the following output slots:

Y(DatasetExperiment)

Details

This object makes use of functionality from the following packages:

  • Rtsne

Inheritance

A tSNE object inherits the following struct classes:

[tSNE] >> [model] >> [struct_class]

References

Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne.

van der Maaten L, Hinton G (2008). "Visualizing High-Dimensional Data Using t-SNE." Journal of Machine Learning Research, 9, 2579-2605.

van der Maaten L (2014). "Accelerating t-SNE using Tree-Based Algorithms." Journal of Machine Learning Research, 15, 3221-3245.

Examples

M = tSNE(
      dims = 2,
      perplexity = 30,
      max_iter = 1000,
      theta = 0.5,
      check_duplicates = FALSE,
      init = NULL,
      eta = 200)

M = tSNE()