pyriemann.embedding.SpectralEmbedding¶
- class pyriemann.embedding.SpectralEmbedding(n_components=2, metric='riemann', eps=None)¶
Spectral embedding of SPD matrices into an Euclidean space.
It uses Laplacian Eigenmaps [1] to embed SPD matrices into an Euclidean space of smaller dimension. The basic hypothesis is that high-dimensional data lives in a low-dimensional manifold, whose intrinsic geometry can be described via the Laplacian matrix of a graph. The vertices of this graph are the SPD matrices and the weights of the links are determined by the Riemannian distance between each pair of them.
- Parameters:
- n_componentsinteger, default=2
The dimension of the projected subspace.
- metricstring | dict, default=’riemann’
The type of metric to be used for defining pairwise distance between SPD matrices.
- epsNone | float, default=None
The scaling of the Gaussian kernel. If none is given it will use the square of the median of pairwise distances between points.
References
[1]Laplacian Eigenmaps for dimensionality reduction and data representation M. Belkin and P. Niyogi, in Neural Computation, vol. 15, no. 6, p. 1373-1396 , 2003
- __init__(n_components=2, metric='riemann', eps=None)¶
Init.
- fit(X, y=None)¶
Fit the model from data in X.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- selfobject
Returns the instance itself.
- fit_transform(X, y=None)¶
Calculate the coordinates of the embedded points.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- X_newndarray, shape (n_matrices, n_components)
Coordinates of embedded matrices.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.