Abstract
Kernel methods are a recent innovation allowing us to perform efficient linear operations in a nonlinear space with the net effect of having nonlinear operations in data space. We derive three different methods of performing Exploratory Projection Pursuit in Kernel space and show on a standard data set that each gives interesting but different projections.
| Original language | English |
|---|---|
| Title of host publication | 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, KES 2000 - Proceedings |
| Editors | R.J. Howlett, L C Jain |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 193-196 |
| Number of pages | 4 |
| ISBN (Electronic) | 0780364007, 9780780364004 |
| DOIs | |
| Publication status | Published (in print/issue) - 2000 |
| Event | 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, KES 2000 - Brighton, United Kingdom Duration: 30 Aug 2000 → 1 Sept 2000 |
Publication series
| Name | 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, KES 2000 - Proceedings |
|---|---|
| Volume | 1 |
Conference
| Conference | 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, KES 2000 |
|---|---|
| Country/Territory | United Kingdom |
| City | Brighton |
| Period | 30/08/00 → 1/09/00 |
Bibliographical note
Publisher Copyright:© 2000 IEEE
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