Name
Abstract | ||
|---|---|---|
Motivated by the fact that data of each cluster are often well captured by distinct features, we propose a clustering approach called multiple kernel self-organizing map (MK-SOM) that integrates multiple kernel learning into the learning procedure of SOM, and carries out cluster-dependent feature selection simultaneously. MK-SOM is developed to reveal the intrinsic relation between features and clusters, and is derived with an efficient optimization procedure. The proposed approach is evaluated on two benchmark datasets, UCI and Caltech-101. The promising experimental results demonstrate its effectiveness. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | optimisation,intrinsic relation,pattern clustering,cluster-dependent feature selection,benchmark datasets,learning (artificial intelligence),mk-som,data analysis,caltech-101 datasets,multiple kernel learning,uci datasets,self-organising feature maps,multiple kernel self-organizing map,optimization procedure,data analysis problems,learning artificial intelligence |
Field | DocType | ISSN |
Graph kernel,Data mining,Pattern recognition,Radial basis function kernel,Feature selection,Computer science,Multiple kernel learning,Self-organizing map,Polynomial kernel,Artificial intelligence,Cluster analysis,Kernel method | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuan-Chieh Huang | 1 | 22 | 4.59 |
| Yen-Yu Lin | 2 | 463 | 39.75 |
| Jie-Zhi Cheng | 3 | 102 | 13.00 |