Cluster-dependent feature selection by multiple kernel self-organizing map

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.