coffeine.covariance_transformers.Snp

class coffeine.covariance_transformers.Snp(rank: int)

Map SPD matrix to Riemannian Wasserstein tangent space.

Riemannian Wasserstein embedding step as described in [1]. Implements Wasserstein metric that is not making the assumption of full-rank inputs. The transform implies a square-root non-linearity.

Parameters:

rankint

The rank to be used for sub-space projection.

References

[1]

Sabbagh, D., Ablin, P., Varoquaux, G., Gramfort, A. and Engemann, D.A., 2019. Manifold-regression to predict from MEG/EEG brain signals without source modeling. Advances in Neural Information Processing Systems, 32.