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t-Distributed Stochastic Neighbor Embedding

modifies
the embeddings using options specified by one or more name-value pair
arguments.`Y`

= tsne(`X`

,`Name,Value`

)

`tsne`

constructs a set of embedded points
in a low-dimensional space whose relative similarities mimic those
of the original high-dimensional points. The embedded points show
the clustering in the original data.

Roughly, the algorithm models the original points as coming
from a Gaussian distribution, and the embedded points as coming from
a Student’s *t* distribution. The algorithm
tries to minimize the Kullback-Leibler divergence between these two
distributions by moving the embedded points.

For details, see t-SNE.