Balanced Accuracy,Center Of Mass,Covariance Matrix,Data Sources,Domain Adaptation,EEG Data,Electrode,Estimated Covariance Matrix,Euclidean Space,Geodesic,Geometric ...

This limitation hinders the representation learning capability of the models. To address this issue, this paper focuses on how to better extend the representation learning from a single space to ...

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python ...

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python ...

The Fisher kernel (4b), on the other hand, respects the structure of the parameters in their original Riemannian manifold by working in the gradient space ... the Fisher kernel outperformed the other ...

Model-based Distributionally Robust Optimisation: Bayesian Ambiguity Sets and Model Misspecification ...