High‐Dimensional Data
Abstract
Chapter 11 addresses the fact that high-dimensional Euclidean space has properties that conflict with intuition obtained from two- and three-dimensional Euclidean space. The chapter covers searching for explicit or implicit mappings of a high-dimensional feature space into a lower-dimensional space and methods that view high-dimensional data as a bias-variance problem.
Fields of Study & Subjects
Face and Expression Recognition
Stochastic Gradient Optimization Techniques
Statistical Methods and Applications
Publication details
- Journal / source
- Machine Learning
- Pages
- 235–266
- Publication date
- 22 January 2026
- Language
- EN