Abstract
Principal curves, like principal components, are a tool used in multivariate analysis for ends like feature extraction. Defined in their original form, principal curves need not exist for general distributions. The existence of principal curves with bounded length and a learning algorithm for such curves for any distribution that satisfies some minimal regularity conditions has been shown. We define principal curves with bounded turn, show that they exist, and present a learning algorithm for them.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 321 |
| Number of pages | 1 |
| Journal | IEEE International Symposium on Information Theory - Proceedings |
| State | Published - 2000 |
| Event | 2000 IEEE International Symposium on Information Theory - Serrento, Italy Duration: Jun 25 2000 → Jun 30 2000 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics