skada.MultiLinearMongeAlignment

skada.MultiLinearMongeAlignment(base_estimator=None, reg=1e-08, bias=True, test_time=False)

MultiLinearMongeAlignment pipeline with adapter and estimator.

The method is a simplified extension of [29] using the Bures-Wasserstein distance and mapping of [7] to align multiple source domains to a barycenter. The sued of barycenter alignment with gaussien assumption was proposed in [30].

Parameters:

base_estimatorobject, optional (default=None)

The base estimator to fit on the target dataset.

regfloat, optional (default=1e-08)

Regularization parameter added to the diagonal of the covariance.

biasbool, optional (default=True)

Estimate bias.

test_timebool, optional (default=False)

If True, the estimator can be updated at test time to map new target domains unseen during training

Returns:

pipelinePipeline

Pipeline containing MultiLinearMongeAlignment adapter and base estimator.

References

[29]

Montesuma, Eduardo Fernandes, and Fred Maurice Ngole Mboula. "Wasserstein barycenter for multi-source domain adaptation." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16785-16793. 2021.

[7]

Flamary, R., Lounici, K., & Ferrari, A. (2019). Concentration bounds for linear monge mapping estimation and optimal transport domain adaptation. arXiv preprint arXiv:1905.10155.

[30]

Gnassounou, Theo, Rémi Flamary, and Alexandre Gramfort. "Convolution Monge Mapping Normalization for learning on sleep data." Advances in Neural Information Processing Systems 36 (2024).