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Kernel pls-svc for linear and nonlinear discrimination
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Matthews, Bryan Trejo, Leonard J. Rosipal, Roman |
| Copyright Year | 2003 |
| Description | A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram. |
| File Size | 878939 |
| Page Count | 8 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20060019228 |
| Archival Resource Key | ark:/13960/t9c58jz4j |
| Language | English |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Mathematical And Computer Sciences (general) Correlation Classifications Kernel Functions Least Squares Method Principal Components Analysis Nonlinearity Electroencephalography Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
