Comparing Sparse and Non-Sparse Multiple Kernel Learning

Contributions • Generalization of [1] to arbitrary convex loss functions and arbitrary norms. • Simple optimization procedure based on an analytical update of the kernel weights. • Toy experiment gives insight in the trade-off between sparsity and accuracy in sparse and non-sparse scenarios. • New large-scale runtime experiments show efficiency of inter-leaved optimization approaches.