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Found 7 papers, 4 papers with code
Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
Natural Bayesian Cramér-Rao Bound with an Application to Covariance Estimation
In this paper, we propose to develop a new Cram\'er-Rao Bound (CRB) when the parameter to estimate lies in a manifold and follows a prior distribution.
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.
Learning Graphical Factor Models with Riemannian Optimization
Graphical models and factor analysis are well-established tools in multivariate statistics.
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.
