Geometric Metric Learning

Geometric metric learning aims to learn distance metrics that better capture the underlying geometry of data, improving the performance of various machine learning tasks. Current research focuses on developing robust algorithms, such as those leveraging Riemannian geometry and M-estimators, to handle noisy or mislabeled data, and on integrating metric learning with decision trees and deep neural networks. These advancements enhance the efficiency and accuracy of classification and other pattern recognition tasks, impacting fields ranging from computer vision to bioinformatics.

Papers

April 23, 2022

A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree
Dan Li, Songcan Chen
Decision Tree Splitting Algorithm Metric Tensor Geometric Metric Learning

February 23, 2022

Robust Geometric Metric Learning
Antoine Collas, Arnaud Breloy, Guillaume Ginolhac, Chengfang Ren, Jean-Philippe Ovarlez
Metric Learning Covariance Matrix Geometric Metric Learning

January 23, 2022

Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
Timely Survey Probabilistic Model Metric Learning Spectral Feature Tutorial Review Deep Metric Learning Geometric Metric Learning

Geometric Metric Learning

Papers

A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree

Robust Geometric Metric Learning

Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey