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.