Name
Abstract | ||
|---|---|---|
Discriminant functions calculated by Support Vector Machines (SVMs) define in a computationally efficient way projections of high-dimensional data on a direction perpendicular to the discriminating hyperplane. These projections may be used to estimate and display posterior probability densities . Additional directions for visualization and dimensionality reduction are created by repeating the linear discrimination process in a space orthogonal to already defined projections. This process allows for an efficient reduction of dimensionality and visualization of data, at the same time improving classification accuracy of a single discriminant function. Visualization of real and artificial data shows that transformed data may not be separable and thus linear discrimination will completely fail, but the nearest neighbor or rule-based methods in the reduced space may still provide simple and accurate solutions. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87536-9_36 | ICANN (1) |
Keywords | Field | DocType |
efficient reduction,support vector machines,space orthogonal,dimensionality reduction,high-dimensional data,reduced space,linear discrimination process,artificial data shows,linear discrimination,single discriminant function,discriminant function,support vector machine,rule based,nearest neighbor,probability density,high dimensional data | k-nearest neighbors algorithm,Dimensionality reduction,Pattern recognition,Visualization,Computer science,Support vector machine,Posterior probability,Curse of dimensionality,Artificial intelligence,Hyperplane,Machine learning,Principal component analysis | Conference |
Volume | ISSN | Citations |
5163 | 0302-9743 | 5 |
PageRank | References | Authors |
0.51 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| tomasz maszczyk | 1 | 42 | 5.29 |
| Włodzisław Duch | 2 | 291 | 28.95 |