TSNE
t-distributed Stochastic Neighbor Embedding: a popular non-linear method for visualising high-dimensional data in 2-D/3-D.
The examples/manifold/tsne.cpp program embeds three 4-D Gaussian clusters into 2-D and renders them with SkigenPlot:
Algorithm
Models pairwise similarities as conditional probabilities in both spaces and minimises their KL divergence by gradient descent, using a heavy-tailed Student-t kernel in the embedding to avoid crowding. v1.1.0 implements the exact (non-Barnes-Hut) gradient.
Constructor
Skigen::TSNE<Scalar> model(int n_components = 2, Scalar perplexity = 30.0, Scalar learning_rate = 200.0, int max_iter = 1000, uint64_t random_state = 0);
Parameters
| Parameter | Default | Description |
|---|---|---|
n_components | 2 | Embedding dimensionality. |
perplexity | 30.0 | Effective neighbourhood size. |
learning_rate | 200.0 | Gradient-descent step size. |
max_iter | 1000 | Optimisation iterations. |
Methods
| Method | Description |
|---|---|
fit_transform(X) | Return the embedding. |
Fitted Attributes
| Accessor | Description |
|---|---|
kl_divergence() | Final KL divergence. |
n_iter() | Iterations run. |
Example
Skigen::TSNE<double> tsne(2, 30.0);
auto Y = tsne.fit_transform(X);
This estimator is checked by the parity suite. See the generator tests/parity/generate_manifold_reference.py and the reference fixtures in tests/parity/data/tsne/, exercised by tests/parity/parity_manifold.cpp.
For full signatures see the TSNE API Reference.