Wasserstein discriminant analysis

DOI10.1007/s10994-018-5717-1zbMath1480.62125arXiv1608.08063OpenAlexW2509351257MaRDI QIDQ1722729

Nicolas Courty, Rémi Flamary, Marco Cuturi, Alain Rakotomamonjy

Publication date: 18 February 2019

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.08063

zbMATH Keywords

Wasserstein distanceoptimal transportlinear discriminant analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Probabilistic measure theory (60A10)

Related Items (13)

Minimax estimation of smooth densities in Wasserstein distance ⋮ Semi-discrete optimal transport: hardness, regularization and numerical solution ⋮ Estimation of Wasserstein distances in the spiked transport model ⋮ On quantum optimal transport ⋮ An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs ⋮ Unnamed Item ⋮ Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case ⋮ Differentiation and regularity of semi-discrete optimal transport with respect to the parameters of the discrete measure ⋮ Statistical inference for Bures-Wasserstein barycenters ⋮ CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data ⋮ Minimax estimation of smooth optimal transport maps ⋮ Coupling matrix manifolds assisted optimization for optimal transport problems ⋮ A sparse control approach to optimal sensor placement in PDE-constrained parameter estimation problems

Uses Software

• UCI-ml
• Manopt
• Pymanopt
• DeCAF
• t-SNE
• LMNN
• Caltech-256
• POT

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Computational Optimal Transport: With Applications to Data Science
• Two-stage image denoising by principal component analysis with local pixel grouping
• An overview of bilevel optimization
• Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
• Manopt, a Matlab toolbox for optimization on manifolds
• Dimension Reduction: A Guided Tour
• Learning Deep Architectures for AI
• The Sinkhorn–Knopp Algorithm: Convergence and Applications
• Optimization Problems with Perturbations: A Guided Tour
• Iterative Bregman Projections for Regularized Transportation Problems
• Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation
• Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
• Optimal Transport
• The elements of statistical learning. Data mining, inference, and prediction
• Choosing multiple parameters for support vector machines

This page was built for publication: Wasserstein discriminant analysis