Multi-task support vector machines for feature selection with shared knowledge discovery
We efficiently select discriminative features for each task by restricting feature selection matrix for each task to be sparse. Relatedness of features among multiple tasks has been also taken into account by mining the shared structures across all tasks. Instead of directly minimizing the rank of a matrix, we propose to minimize trace norm of a matrix.
feature selection trace norm multi-task learning low-rank