brainspace.gradient.embedding.laplacian_eigenmaps¶
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brainspace.gradient.embedding.
laplacian_eigenmaps
(adj, n_components=10, norm_laplacian=True, random_state=None)[source]¶ Compute embedding using Laplacian eigenmaps.
Adapted from Scikit-learn to also provide eigenvalues.
Parameters: Returns: - v (2D ndarray, shape (n, n_components)) – Eigenvectors of the affinity matrix in same order. Where n is the number of rows of the affinity matrix.
- w (1D ndarray, shape (n_components,)) – Eigenvalues of the affinity matrix in ascending order.
References
- Belkin, M. and Niyogi, P. (2003). Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6): 1373-96. doi:10.1162/089976603321780317