Name
Title | ||
|---|---|---|
A multi-class classifying algorithm based on nonlinear dimensionality reduction and support vector machines |
Abstract | ||
|---|---|---|
Many problems in pattern classifications involve some form of dimensionality reduction. ISOMAP is a representative nonlinear dimensionality reduction algorithm, which can discover low dimensional manifolds from high dimensional data. To speed ISOMAP and decrease the dependency to the neighborhood size, we propose an improved algorithm. It can automatically select a proper neighborhood size and an appropriate landmark set according to a stress function. A multi-class classifier with high efficiency is obtained through combining the improved ISOMAP with SVM. Experiments show that the classifier presented is effective in fingerprint classifications. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539087_90 | ICNC (1) |
Keywords | DocType | Volume |
low dimensional manifold,support vector machine,multi-class classifying algorithm,neighborhood size,multi-class classifier,improved algorithm,improved isomap,high efficiency,representative nonlinear dimensionality reduction,high dimensional data,proper neighborhood size,dimensionality reduction,nonlinear dimensionality reduction | Conference | 3610 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-28323-4 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukui Shi | 1 | 11 | 4.33 |
| Qing Wu | 2 | 351 | 76.78 |
| Xueqin Shen | 3 | 9 | 1.56 |
| Pilian He | 4 | 29 | 7.46 |