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Advanced t-SNE parameters

Perplexity

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t-SNE preserves the local structure of the data by focusing on the distances between each point and its nearest neighbors. Perplexity can be thought of as the number of nearest neighbors being considered.  The optimal perplexity depends on the size and density of the data. Generally, a larger and/or more dense data set will benefit from a higher perplexity (Figure 2). Default is 30. The range of possible values is 3 to 100.

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Numbered figure captions
SubtitleText Setting t-SNE perplexity to 5 (left), 30 (middle), 100 (right). AnchorName t-SNE perplexity

Number of

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iterations

t-SNE uses an iterative algorithm to optimize the low-dimensional representation. More iterations will result in a more accurate embedding to a point, but will take longer to run. Default is 1000.

Random generator

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seed

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Several parts of t-SNE utilize a random number generator to provide an initial value. Default is 1. To reproduce the results, use the same random seed at all runs.

Initialize output values at random

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If this option is disabled, the initial values are assigned using the largest principal components extracted from the raw data. Default is enabled.  

Distance metric

The metric to use when computing distances in high-dimensional space. Default is Euclidean.

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