Multiple Kernel k-Means Clustering by Selecting Representative Kernels.

To cluster data that are not linearly separable in the original feature space, k -means clustering was extended to the kernel version. However, the performance of kernel k -means clustering largely depends on the choice of the kernel function. To mitigate this problem, multiple kernel learning has been introduced into the k -means clustering to obtain an optimal kernel combination for clustering.

Ensemble Pruning Based on Objection Maximization With a General Distributed Framework.

Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors, while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy.