Name
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. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/KES.2000.885790 | KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS |
Keywords | Field | DocType |
unsupervised learning,principal component analysis,projection pursuit,computational intelligence,kernel,artificial intelligence,kernel method,covariance matrix,linear operator | Kernel (linear algebra),Mathematical optimization,Principal component regression,Projection pursuit,Radial basis function kernel,Kernel embedding of distributions,Computer science,Algorithm,Kernel principal component analysis,Polynomial kernel,Kernel method | Conference |
Citations | PageRank | References |
1 | 0.45 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donald Macdonald | 1 | 117 | 9.79 |
| Colin Fyfe | 2 | 508 | 55.62 |
| Darryl Charles | 3 | 85 | 16.25 |