SpectralEmbedding
Laplacian eigenmaps: a spectral embedding from the eigenvectors of the graph Laplacian of a neighbourhood affinity graph.
Algorithm
Constructs an affinity graph (nearest-neighbours by default), forms the normalised Laplacian, and embeds using its smallest non-trivial eigenvectors.
Constructor
Skigen::SpectralEmbedding<Scalar> model(int n_components = 2, int n_neighbors = 5);
Parameters
| Parameter | Default | Description |
|---|---|---|
n_components | 2 | Embedding dimensionality. |
n_neighbors | 5 | Neighbours in the affinity graph. |
Methods
| Method | Description |
|---|---|
fit_transform(X) | Return the embedding. |
Fitted Attributes
| Accessor | Description |
|---|---|
affinity_matrix() | The constructed affinity graph. |
Example
Skigen::SpectralEmbedding<double> se(2, 5);
auto Y = se.fit_transform(X);
Verified against scikit-learn
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/spectral_embedding/, exercised by tests/parity/parity_manifold.cpp.
API Reference
For full signatures see the SpectralEmbedding API Reference.