Self-organising maps
Abstract
This chapter introduces self-organizing maps, which are especially useful for clustering data that lies on a non-linear low-dimensional manifold in high-dimensional space. It shows how the resulting low-dimensional map can be visualised in the high-dimensional data space using a tour, so that the model fit can be assessed.
Fields of Study & Subjects
Neural Networks and Applications
Face and Expression Recognition
Topological and Geometric Data Analysis
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Pages
- 108–110
- Publication date
- 11 March 2026
- Language
- EN
- Document type
- Book chapter