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Found 7 papers, 3 papers with code
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability.
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited labeled signals in the target dataset.
The Fisher-Rao geometry of CES distributions
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric.
Entropic Wasserstein Component Analysis
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data.
Parametric information geometry with the package Geomstats
We introduce the information geometry module of the Python package Geomstats.
Riemannian optimization for non-centered mixture of scaled Gaussian distributions
The proposed Riemannian gradient descent algorithm is leveraged to solve this second minimization problem.
Robust Geometric Metric Learning
This paper proposes new algorithms for the metric learning problem.
