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# t-SNE#

T-distributed Stochastic Neighbor Embedding is a two-stage non-linear manifold learning algorithm based on Batch Gradient Descent that seeks to maintain the distances between samples in low-dimensional space. During the first stage (early stage) the distances are exaggerated to encourage more pronounced clusters. Since the t-SNE cost function (KL Divergence) has a rough gradient, momentum is employed to help escape bad local minima.

Note: T-SNE is implemented using the exact method which scales quadratically in the number of samples. Therefore, it is recommended to subsample datasets larger than a few thousand samples.

Interfaces: Transformer, Verbose

Data Type Compatibility: Depends on distance kernel

## Parameters#

# Param Default Type Description
1 dimensions 2 int The number of dimensions of the target embedding.
2 rate 100.0 float The learning rate that controls the global step size.
3 perplexity 30 int The number of effective nearest neighbors to refer to when computing the variance of the distribution over that sample.
4 exaggeration 12.0 float The factor to exaggerate the distances between samples during the early stage of embedding.
5 epochs 1000 int The maximum number of times to iterate over the embedding.
6 min gradient 1e-7 float The minimum norm of the gradient necessary to continue embedding.
7 window 10 int The number of epochs without improvement in the training loss to wait before considering an early stop.
8 kernel Euclidean Distance The distance kernel to use when measuring distances between samples.

Return the magnitudes of the gradient at each epoch from the last embedding:

``````public steps() : array
``````

## Example#

``````use Rubix\ML\Embedders\TSNE;
use Rubix\ML\Kernels\Distance\Manhattan;

\$embedder = new TSNE(3, 10.0, 30, 12.0, 500, 1e-6, 10, new Manhattan());
``````
• L. van der Maaten et al. (2008). Visualizing Data using t-SNE.
• L. van der Maaten. (2009). Learning a Parametric Embedding by Preserving Local Structure.