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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.

Generate mapping error statistics

If checked, mapping error information will be available in the task report. Default is disabled.

Generate t-SNE table

Output a t-SNE table data node that can be downloaded. The 2D t-SNE coordinates are labeled Feature 1 and Feature 2; the 3D t-SNE coordinates are labeled Feature 3, 4, and 5.  Default is disabled.

PCA: Number of principal components 

t-SNE uses principal components as its input. The number of principal components to use is set here. 

We recommend using the PCA task to determine the optimal number of principal components for your data.  Default is 50.

PCA: Features contribute 

Options are equally or by variance. Feature values can be standardized prior to PCA so that the contribution of each feature does not depend on its variance. To standardize, choose equally. To take variance into account and focus on the most variable features, choose by variance.  Default is by variance.

Normalization: Log transform data

You can choose to log transform the data prior to running PCA as part of t-SNE.  Default is disabled.

Normalization: Log base

If you are normalizing the data, choose a log base.  Default is 2 when Log transform data is enabled.

Normalization: Log offset

If you are normalizing the data, choose an offset.  Default is 1 when Log transform data is enabled.

References

[1] 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.

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